ThoughtDAG turns LLM chats into editable graph maps

ThoughtDAG addresses a gap in how LLM conversations are typically managed: chat interfaces are linear and ephemeral, making it difficult to inspect, verify, or systematically refine reasoning. By representing conversations as directed acyclic graphs, the tool makes the flow of context explicit and editable. The core principle—"wires are the context"—means the human retains full visibility and control over what information the model receives at each step. This design philosophy applies across features: merging nodes represents human judgment about which ideas compress into a higher-level insight, weaving highlights into cited prose ensures every sentence traces back to the user's own marked passages, not the model's selection, and replaying upstream edits with token estimates lets users reason about the cost and validity of changes.

The tool's support for reading PDFs directly into maps and its local-first architecture (automatic folder backup, PDFs never leaving the user's machine) suggest an intent to keep knowledge work grounded in primary sources and user control. The read-only share feature enables collaboration without requiring accounts or server-side storage, and backward-compatible export to Markdown provides an escape hatch independent of the ThoughtDAG platform itself.