Hi HN, I built ThoughtDAG around one rule: wires are the context.
Each question and answer is a node. When you ask from a node, only its wired upstream nodes are included in the model request. Delete an edge, regenerate, and that branch leaves the model's actual context, not just the visualization.
The interface is intentionally human-controlled. I'm testing whether explicit context control is useful for long-running research, or whether most people would rather delegate memory selection to retrieval.
It is MIT licensed, local-first, supports Ollama and OpenAI-compatible endpoints, and includes PDF clipping with page provenance.
What about cache? When you change the context the prefill stage will be much slower?
Hi HN, I built ThoughtDAG around one rule: wires are the context.
Each question and answer is a node. When you ask from a node, only its wired upstream nodes are included in the model request. Delete an edge, regenerate, and that branch leaves the model's actual context, not just the visualization.
The interface is intentionally human-controlled. I'm testing whether explicit context control is useful for long-running research, or whether most people would rather delegate memory selection to retrieval.
It is MIT licensed, local-first, supports Ollama and OpenAI-compatible endpoints, and includes PDF clipping with page provenance.
GitHub: https://github.com/chenxiachan/thoughtdag
I'd especially appreciate criticism of the interaction model and onboarding.