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Show HN: ThoughtDAG – An editable context graph for LLM conversations

CHAT HIDES CONTEXT.

THE GRAPH IS THE CONTEXT.

Linear conversation

Editable context graph

Same prompt · different context

AI conversation 87 messages

Compare three research paths.

Start with the first. Its advantage is…

What if the core hypothesis fails?

Consider another explanation…

Also, what should I eat tonight?

There is a new restaurant nearby.

The history is here. Which parts enter the next request?

research-paper.pdfp.7

Results

The effect appears only in the experimental condition.

Selected from the page

Clipped passageresearch-paper.pdf · p.7

The effect appears only in the experimental condition

Source linked · not wired yet

Asked from sourceresearch-paper.pdf · p.7

What does this evidence actually mean?

The source is in context

Unrelated branchdetour

What should I eat tonight?

This history should not enter the research summary.

Still connected

Polluted summary 3 sources

Research summary… also, consider hot pot for dinner.

The prompt stayed the same. Polluted context changed the answer.

Includes unrelated branch

Will send1,284 tokens

Preview what the model will receive

Incoming ancestors:

Research question Evidence A Dinner detour

After deleting the orange edge: −47 tokens

Context diff−47 tok

The dinner detour left context

Same prompt · regenerate

Same promptask again

Give me a bullet-point summary

The words are identical. Only one edge changed.

Reproducible context

Clean answer 2 sources

One: record the database version. Two: use independent reviewers. Three: resolve conflicts with a third reviewer.

The unrelated dinner suggestion is gone.

Answer updated in place

One ruleThoughtDAG

Wires are context.

No hidden memory selector. What the model sees, why, and what was removed stay visible in the graph.

Visible Editable Inspectable

01 · The problem

Chat history is long. Context is still invisible.

The interface shows what was said, not which history enters the next request.

02 · Externalize

Ask from the source. Clip what matters.

Ask from a selected passage, or turn a passage or figure into its own source-linked node. Provenance stays attached; context remains yours to wire.

03 · Inspect

Before sending, inspect what the model will read.

Preview source nodes, order, and token count. Context is no longer a hidden decision.

04 · Edit

Delete one edge. Ask the same question again.

The removed branch really leaves the request. The answer changes with the context.

05 · The protocol

Most canvases organize information. ThoughtDAG edits context.

You decide what enters and leaves. The graph is the context protocol before generation.

1 / 5 Invisible context