Even if you are as averse to semver as I used to be in the course of my programming activity, you can still think of open source software distribution as something that used to follow a fixed number of steps. There is a branch where developments happen, and this branch oftentimes happens to be not really ready for reliable work. Then you freeze the developments for a certain amount of time (even if, in the meantime, the work can continue on some new unstable branch), fix bugs, ask people to test it. At some point the number of bug reports starts to drop, your team and your users start to believe there are no longer obvious critical flaws that are easy to discover in the next few weeks: then you call the branch 2.4 or whatever, and that's it.
However now, with AI coding, it's not just development that has changed, but also the act itself of using software is affected: it is not just you that can ask an AI to do certain changes to the software, but also the recipient of the software itself. This is obvious in the domains where a piece of software has its main user base among programmers, but this is also true in general, as more and more technologically inclined users have AI access and coding agents.
Because of this change, the idea of just having a stable branch with everything polished, and an unstable branch where everything is a work in progress, may no longer be the right way to do things. A code repository can also be a finished product, but could be even more useful if it is a template for how to do things around a given problem. Maybe the user will modify the code in order to specialize it for a specific set of requirements, hardware, specific problems to solve. Also, what is too unstable or unproven for the general public may be the right thing for another set of users.
Take the example of Redis. For weeks now I have been iterating on a PR that provides strong memory savings for sorted sets. This work, if accepted, will hit every user of Redis, from people that don't have any idea about how Redis works, to users that maybe even contributed code in the course of years. From use cases that are trivial to use cases where a 50% memory saving on sorted sets could mean cutting a big slice of the cloud bill every year. For this last kind of user, having the final product (after all the testing and changes of design I'm doing to refine something that "just works", with the risk that maybe it will not even enter the code base) may be less interesting than having a 95%-ready branch since day zero. It is code they can test, adapt, iterate on, even specialize more for the problem at hand.
Maybe DwarfStar is an even more telling example of how code repositories should be good examples more than finished products covering every piece of the features matrix. With local inference you have, in the specific case of DwarfStar, many kinds of GPUs, models, server mode, agent mode, CLI, SSD streaming, tensor and pipeline distributed execution. To test everything everywhere is complicated. Yet, once you have two solid examples of tensor parallel graph execution, a strong coding agent can infer how to implement the same thing for other backend/model pairs. Similarly, once you have an engine that supports two models well enough, a third can be implemented in an almost automatic way, using the existing code base as a guardrail for coding agents in order to guide the implementation.
This does not mean that a project like DwarfStar should not work out of the box, but that it could focus on supporting very well a set of features that can be extrapolated to a larger amount of possible situations that the users can cover themselves. It also means another thing: that main and unstable are no longer enough. Many experimental branches could be an integral part of the project. For instance, yesterday the Laguna S.1 model was released. It looks interesting on paper, however: will it really be good enough? Will the new DeepSeek v4 Flash checkpoints make it not really relevant for DwarfStar? It is too early to say. However, to collectively form an idea, publishing a branch with this model implementation is a good middle ground: people will try it, will refine it with their coding agents, and the community can collectively form an idea about how merge-worthy it is. Moreover, today I noticed how, thanks to the rails formed by the corpus of the code inside DwarfStar, the implementation was written in about two hours by GPT 5.6 Sol automatically. Implementing DS4 and GLM5.2 cost me a lot of steering, reading the model card and the details of the implementation of the attention of those models. Now it just worked. GPT 5.6 is more powerful but it also found a lot of good examples inside the existing source code.
Software today is more malleable than ever. In some way this means that it can be released in a more fluid way. Also, it means that the documentation itself should not be just good for humans, but also for coding agents to understand how to change the system. How this will evolve exactly, and what the right point of balance between the different dimensions of stability, usability, and features will be, is not clear to me, but I believe we developers need to keep our eyes open to see where all this is headed. Comments
However now, with AI coding, it's not just development that has changed, but also the act itself of using software is affected: it is not just you that can ask an AI to do certain changes to the software, but also the recipient of the software itself. This is obvious in the domains where a piece of software has its main user base among programmers, but this is also true in general, as more and more technologically inclined users have AI access and coding agents.
Because of this change, the idea of just having a stable branch with everything polished, and an unstable branch where everything is a work in progress, may no longer be the right way to do things. A code repository can also be a finished product, but could be even more useful if it is a template for how to do things around a given problem. Maybe the user will modify the code in order to specialize it for a specific set of requirements, hardware, specific problems to solve. Also, what is too unstable or unproven for the general public may be the right thing for another set of users.
Take the example of Redis. For weeks now I have been iterating on a PR that provides strong memory savings for sorted sets. This work, if accepted, will hit every user of Redis, from people that don't have any idea about how Redis works, to users that maybe even contributed code in the course of years. From use cases that are trivial to use cases where a 50% memory saving on sorted sets could mean cutting a big slice of the cloud bill every year. For this last kind of user, having the final product (after all the testing and changes of design I'm doing to refine something that "just works", with the risk that maybe it will not even enter the code base) may be less interesting than having a 95%-ready branch since day zero. It is code they can test, adapt, iterate on, even specialize more for the problem at hand.
Maybe DwarfStar is an even more telling example of how code repositories should be good examples more than finished products covering every piece of the features matrix. With local inference you have, in the specific case of DwarfStar, many kinds of GPUs, models, server mode, agent mode, CLI, SSD streaming, tensor and pipeline distributed execution. To test everything everywhere is complicated. Yet, once you have two solid examples of tensor parallel graph execution, a strong coding agent can infer how to implement the same thing for other backend/model pairs. Similarly, once you have an engine that supports two models well enough, a third can be implemented in an almost automatic way, using the existing code base as a guardrail for coding agents in order to guide the implementation.
This does not mean that a project like DwarfStar should not work out of the box, but that it could focus on supporting very well a set of features that can be extrapolated to a larger amount of possible situations that the users can cover themselves. It also means another thing: that main and unstable are no longer enough. Many experimental branches could be an integral part of the project. For instance, yesterday the Laguna S.1 model was released. It looks interesting on paper, however: will it really be good enough? Will the new DeepSeek v4 Flash checkpoints make it not really relevant for DwarfStar? It is too early to say. However, to collectively form an idea, publishing a branch with this model implementation is a good middle ground: people will try it, will refine it with their coding agents, and the community can collectively form an idea about how merge-worthy it is. Moreover, today I noticed how, thanks to the rails formed by the corpus of the code inside DwarfStar, the implementation was written in about two hours by GPT 5.6 Sol automatically. Implementing DS4 and GLM5.2 cost me a lot of steering, reading the model card and the details of the implementation of the attention of those models. Now it just worked. GPT 5.6 is more powerful but it also found a lot of good examples inside the existing source code.
Software today is more malleable than ever. In some way this means that it can be released in a more fluid way. Also, it means that the documentation itself should not be just good for humans, but also for coding agents to understand how to change the system. How this will evolve exactly, and what the right point of balance between the different dimensions of stability, usability, and features will be, is not clear to me, but I believe we developers need to keep our eyes open to see where all this is headed. Comments