Infinite Graph
Turn documents and ideas into a knowledge graph you explore
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Infinite Graph - Turn documents and ideas into a knowledge graph you explore

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Infinite Graph turns documents, notes, and complex information into an explorable knowledge graph. Instead of another wall of AI text, it reveals relationships, preserves evidence, and surfaces opportunities you may have missed. With GPT-6 Astra, it can investigate complex sources, build explanation graphs, and propose new connections for you to verify, accept, or reject.

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Maker
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Hey Product Hunt — I’m the solo maker behind Infinite Graph. I started building it because I kept running into the same problem: the more information I collected, the harder it became to understand how any of it actually fit together. Folders preserve location. Search retrieves what you already know to look for. AI chat can summarize enormous amounts of information, but the result is usually more text — useful in the moment, difficult to preserve as a structure you can return to and reason with later. Infinite Graph takes a different approach. It turns documents, notes, and ideas into a visual knowledge graph where concepts remain connected to their context and evidence. The graph is meant to be something you can navigate, reorganize, and gradually build into a representation of what you know. For this launch, I’ve been exploring a further question with GPT-6 Astra: What if AI didn’t just answer questions about your knowledge, but helped you discover the questions and connections you hadn’t noticed? Astra can investigate complex information, construct an explanation graph around what matters, and surface potential connections as Opportunities. Those suggestions don’t silently become facts: you can inspect the evidence and decide whether to accept, edit, or reject them. That reflects the principle I’ve tried to keep throughout Infinite Graph: AI assists. You decide. I’m especially interested in feedback on where this becomes genuinely useful — research, learning, codebases, writing, or something I haven’t considered yet. Thanks for taking a look.
OpenAI
GPT-6 Astra Challenge
What became possible in your product with Astra that was not practical before?
Maker
Astra changed the feasible scope of what I could build as a solo founder. Infinite Graph combines a graph workspace, local-first persistence, cloud sync, document and code import, retrieval, AI-assisted discovery, and a performance-sensitive renderer. Building and debugging those systems normally requires substantial engineering bandwidth. With Astra, I could work at the level of architecture rather than isolated code generation: investigate performance regressions across the codebase, trace bottlenecks through React Flow and state propagation, design incremental persistence and publication paths, challenge architectural assumptions, implement changes, and verify them against tests and benchmarks. The result is not a prototype generated from a prompt. Astra made it practical for one person to iterate on a systems-heavy product with the engineering depth of a much larger project. It effectively increased the complexity I could responsibly own, test, and ship.