For the last year we've been building AI workspaces. With every new product, we found ourselves rebuilding the same context layer and infrastructure. We tried existing solutions, but we'd either have to lock into one provider or have to manage the storage infrastructure.What we wanted was an API that's stateful and isn't tied to a specific provider. So that we didn't have to think about...
Appreciate the feedback. We agree there's definitely more work to be done on exactly how trees are represented to the user.
When I was using twigg to build itself, I often just used the side panel branch off when I needed to instead of using the tree diagram. The tree then kind of built itself.
Would be interested to hear if you prefer having the tree up on screen, or if you prefer the 'branch to the side' approach.
Very interesting you bring this up. It was quite a big point of discussion whilst jamie and I were building.
One of the big issues we faced with LLMs is that their attention gets diluted when you have a long chat history. This means that for large amounts of context, they often can't pick out the details your prompt relates to. I'm sure you've noticed this once your chat gets very long.
Instead of trying to develop an automatic system to descide what context your prompt should use (I.e which branch you're on), we opted to make organising your tree a very deliberate action. This gives you a lot more control over what the model sees, and ultimately how good the responses. As a bonus, if a model if playing up, you can go in and change the context it has by moving a node or two about.
Really good point though, and thanks for asking about it. I'd love to hear if you have any thoughts on ways you could get around it automatically.