Applying fork/join model and git-style cherry picking to AI conversations

In this post we introduce using the fork/join concurrency pattern combined with the git-style cherry picking to manage and use LLM conversations.  

These are loose conceptual connections, so please put down the pitchfork (pun intended).

Just as in git you can cherry-pick a commit from one branch to create a new head on another branch, you can pick conversations across sessions (including between different models) to build context to drive LLM conversations.

You can also create a fork of a session, this creates a parallel set of messages that are detached from, ie forked off the main session within which they run.

You can create new messages that join an existing fork.

This is a nice capability, the degree to which it will prove useful is yet to be determined. One of the possibilities is giving the user an ability to create long term "memory" from AI conversations and being able to easily reuse that memory (set of messages) without the need to rebuild context.

The ability to label forks is especially helpful in letting user know what the fork represents and thus offers the potential of greater utility. Below is a demo of this feature: