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

chenxiachan.github.io

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

#61

Post by chatchan »

DenisM wrote: post #58
chatchan wrote: post #48

Earlier quoted context omitted.

I think we need to distinguish between "what the model received" and "why the model generated this answer." ThoughtDAG currently focuses on the former: accurately displaying the context of the incoming request and allowing users to modify it.

Sometimes it’s just an awkward turn of phrase on my part that creates a wrinkle in the conversation. Sometimes agents identify that, and we can work together and direct that, but correction itself eventually loses competition to the original error.

I agree.

Most LLM tools (Claude Web, OpenAI, and their harness) offer re-editable questions. That is how I avoid such problems by myself.

In ThoughtDAG, you can re-edit questions by double-clicking the question. Or edit the answer by clicking the edit icon at the end of each answer text. Or.. you can just remove the connection or delete the node.

That would give you manageable context

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

#64

Post by Xx_crazy420_xX »

Really nice project, i like some of the functionalities you have though of. When thinking of new concepts i sometimes use similar tool which i created https://github.com/Srakai/bushchat, its browser based (in my opinion more convenience). I kind of switched to .md file knowledge base now so i don't use it very often anymore.

For me the most interesting idea around branching is tree rebuilding itself up when source node is modified. For example, if you are drafting a new project and one assumption changes, all subsequent nodes that based on that knowledge get rebuilt.

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

#67

Post by frogperson »

mikeebener wrote: post #30

I looked at the repo and demo canvas. Nice work. Especially liked the 3 semantic zoom tiers and the weave/condense features. If you're enabling for less-technical users consider leading with weave and condense vs. edge deletion. Edge deletion is where the model is powerful but my Mom would get stuck there for instance. The idea that removing a wire changes what the model actually sees might not be obvious. Consider w…

There is no way to delete a highlight, I accidentally deleted the root node trying to remove a highlight, then the undo command wouldn't recall the root node .

very cool ice over all, its earned a spot in my dock for now.

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

#69

Post by chatchan »

Xx_crazy420_xX wrote: post #64

Really nice project, i like some of the functionalities you have though of. When thinking of new concepts i sometimes use similar tool which i created https://github.com/Srakai/bushchat , its browser based (in my opinion more convenience). I kind of switched to .md file knowledge base now so i don't use it very often anymore. For me the most interesting idea around branching is tree rebuilding itself up when source n…

Thank you for sharing! That did occur to me. ThoughtDAG now marks affected downstream answers as needing updates after an upstream node is modified, allowing users to rerun the algorithm in dependency order; alternatively, automatic refresh can be enabled only for specific nodes.

I didn't rebuild the entire tree by default, mainly because modifying earlier nodes might trigger a large number of calls and could overwrite some still valuable intermediate results. Older answers are retained as historical versions for easy comparison.

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

#70

Post by chatchan »

UltraSane wrote: post #62

I've been working on something similar to this using Neo4j so you can control the context with a Cypher query because I really like Cypher. But this visualization is excellent.

Thanks! I'm not very familiar with Neo4j and Cypher yet. How do you control the context using Cypher? Do you manually write queries for each request, or do you select nodes through the interface and then automatically generate queries? I'm also curious about how the graph structure obtained from the query is ultimately transformed into an ordered model context.
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