Xia Chen @ChatchanX
GNI Postdoctoral Fellow @TUM | Human-AI Alignment | Building ThoughtDAG, an open-source canvas where wires are the context. chenxiachan.github.io/thoughtdag/ Munich, Germany Joined September 2017-
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I invited a few friends to try ThoughtDAG with real questions. Each process left a different shape behind. 🧠 One deep thread. Five roads, four dead ends. Three weeks around one question. A literature review season. Export yours: github.com/chenxiachan/th…
Watch on YouTube: youtu.be/PF_uhanaoQg Open source: github.com/chenxiachan/th…
One wrong note changed the LLM’s recommendation. Same model. Same question. I removed one wire from the context graph and asked again. The answer changed. ThoughtDAG makes LLM context visible and editable. Would you control this manually, or automate the cleanup?
Can one wire change an AI answer? In ThoughtDAG, wires are not decoration. They decide what the model actually receives. This is a real API run: remove one edge, regenerate, and watch the context and answer change. Open source: github.com/chenxiachan/th…
This is not hidden model memory. The stale value remained visible in downstream conversation turns. ThoughtDAG made that path inspectable, editable, and reproducible. Case study: chenxiachan.github.io/thoughtdag/sto… Full report: chenxiachan.github.io/thoughtdag/res…
Across 72 model-cases first derailed by pollution: • delete the source: 68/72 repaired • delete + recompute: 71/72 • remove the contaminated subgraph: 72/72 The consequence sometimes mattered more than the original mistake.
I deleted the original mistake from an LLM conversation. Two models still gave the same wrong answer. The error had already been repeated downstream. Removing the source was not enough. A small pilot on how context errors survive:
Yesterday I posted ThoughtDAG on Hacker News. 52 comments later, the idea is the same, but the roadmap is clearer: • show context as a list, not only a graph • fix canvas friction first • explore a read/write API for existing tools github.com/chenxiachan/th…
Most AI chats let you regenerate an answer, but not repair it. If 90% is right, I should be able to fix one sentence and keep thinking. So I made every answer in ThoughtDAG editable. Your correction becomes part of the context downstream. github.com/chenxiachan/th…
I just reserved my Cloudflare Wallet tag: chatchan.cloudflare.pay. Reserve yours now at cloudflare.pay
I Made AI Context Editable — Meet ThoughtDAG youtu.be/YdJJflUxBi0?si… via @YouTube
@beamnxw I do made a tool for graph engineering, called ThoughtDAG
Meet ThoughtDAG, a Graph Engineering tool for your context manageable LLMs interaction
New Graph Engineering Tool for LLMs! ThoughtDAG: the conversation is a graph on an infinite canvas. What the model sees is exactly what you wired in. github.com/chenxiachan/th…
It's open source, local-first, and honestly still an early prototype built by one person. The example canvas needs no signup and no key, so you can just poke at it. I'd love to know what breaks. github.com/chenxiachan/th…
The part I care about most: nothing here runs on its own. No agent quietly rewrites your graph. You read the papers, you highlight what matters, you decide what merges into what. The human stays in the loop, the model stays on the wires.
Last month I lost my best idea in a 60-turn chat with an AI. It was in there somewhere. I just couldn't scroll back to it. I'm a researcher, and I got tired of that. So I spent a few months building a different way to think with a model.
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