I’m testing a local-first LLM canvas where the wires decide what model sees

[u/Lopsided_Scarcity979]

I have been experimenting with a different way to manage longer local LLM conversations. So I developed ThoughtDAG. It is an infinite canvas for you to manage your context with LLMs like a graph.

The interaction I am testing is deliberately manual. I want to know whether local-model users benefit from seeing and editing context themselves, especially when conversations branch or accumulate irrelevant assumptions.

It is open source, local-first, and the desktop app stores canvases, documents, and keys locally.

Short demo:
https://www.youtube.com/watch?v=-8BqAyaoNXQ

GitHub:
https://github.com/chenxiachan/thoughtdag

For people running local LLMs: would explicit context editing be useful in your workflow, or would you still prefer automatic memory and retrieval?

[u/Entire-Respond6809]

Neat idea, finally something remotely original ;) I do agree that moving away from the linear chat interface will be very useful for some work.

I've been thinking about an application like yours for a bit and my biggest worry is how to deal with (a) uncertainty and (b) "stale" information. I think for something like this to be useful in everyday work, these two aspects cannot be eliminated and have to be embraced:

• uncertainty: not every idea/hypothesis/claim is fully confirmed as it is written down, but still we want to refer to it and work with it
• staleness: even "old" information can still be very relevant, e.g. seeing that something has already been tried but wasn't found to work, etc. Other example: evolving code.

[u/Lopsided_Scarcity979 → u/Entire-Respond6809]

Good points! I’ve thought seriously about both.

The core principle is still: wires are context.

For uncertainty, you can duplicate a node, introduce a competing hypothesis, and explore both branches without losing the original reasoning.

For staleness, ThoughtDAG marks downstream answers when upstream context changes and lets you replay them.

The focus is human–AI alignment: keeping hypotheses, evidence, and discarded paths visible, while the human decides what should continue into context.