Show HN: ThoughtDAG – An editable context graph for LLM conversations
chenxiachan.github.io
Show HN: ThoughtDAG – An editable context graph for LLM conversations
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Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#2Hi 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.
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#3What about cache? When you change the context the prefill stage will be much slower?
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#4What about cache? When you change the context the prefill stage will be much slower?
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Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#5This looks like git, doesn't it?
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#6Hi 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…
have you seen other people or project on the same idea ? manipulation history and exploration space of LLM seems to be quite important
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#7What about cache? When you change the context the prefill stage will be much slower?
That's always going to be a trade off with anything like this so I guess it's better to think of it as an alternative to compaction.
Another use case that comes to mind is that sometimes I'll include some detail early in a conversation and I mean it as incidentals information but the AI fixates on it. If I could selectively edit that out rather than start a whole new conversation it would be worth the cache miss.
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#8The basic idea here looks interesting and is easy to understand but what I'm not understanding is why it's a standalone app. Is this supposed to replace e.g. Claude desktop? Or can it plug in to other systems like Claude Code, Codex, Pi?
I don't think I'd want to use it as a standalone app but I would certainly be interested in it as a plugin.
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
#9What about cache? When you change the context the prefill stage will be much slower?
I have not noticed a measurable slowdown in practice so far, including canvases with around a hundred nodes. A request only includes the wired ancestors of the current node, not the entire canvas, so node count alone is not a good measure of prefill cost.
That said, your concern is valid for very long contexts. Editing an early ancestor may reduce prefix-cache reuse, while pruning a branch also makes the resulting prompt shorter. ThoughtDAG does not manage its own KV cache today, so this is something I need to benchmark properly rather than claim is solved.
Have you encountered this mainly with local models or hosted APIs?
Re: Show HN: ThoughtDAG – An editable context graph for LLM conversations
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