However with LLMs you can have them analyze prior transcripts to identify bug sources and so on. I could see it being used. But all theorycrafting. And perhaps motivated reasoning. I’ve always committed early and often and used bisect to find bugs. That places me right away in the squash commits camp because of the method I use to write code.
It's a tag with an UID. It's useful to connect together multiple changes, including possibly several commits across multiple unrelated repositories, as well as other artifacts and actions. It records the causal link, and connects it to telemetry (people do run their own telemetry over agents, especially in team/company setting). Because chances are that, if you have issues with some change made by AI, being able to look up what other changes were made as part of the same session is going to be very helpful.
> That's not even bringing into the fact that there are people who use multiple models, multiple sessions, multiple harnesses across the same commits/changesets. How do you reconcile those alongside people who could have made human changes in the end?
This is exactly how. By tagging changes with session UID, and using it as key to resolve other changes, as well as specific model and harness versions used, if you use telemetry endpoints harnesses tend to offer.
The whole discussion is just people getting tripped over the tag being link-shaped, ie. "https://what.tld/<uid>", instead of "WHAT_ID=<uid>".
The specific model used can't be important because theres a new trending one every couple weeks nowadays. The conversation could be wildly out there and have 10+ different iterations of the same thing in it that led to the final product, etc.
It's not like before AI we were asking people to explain every single iteration they went through in their commit body, or to see their entire internal dialogue.
I don't really understand people's hype around needing to see the conversation they had with their AI. As long as the code itself is self-documented and final decisions are relatively well-documented future agents and humans will pick up on it much faster than reading the entire transcripts anyways.
That's not even bringing into the fact that there are people who use multiple models, multiple sessions, multiple harnesses across the same commits/changesets. How do you reconcile those alongside people who could have made human changes in the end?