Open-Source Visual Wiki Your Coding Agent Writes for You

Hacker News

Hi HN, We’re Ruben, Afnan, and Theo. A couple of days ago we released the early repo for Davia and were surprised by how much feedback came in right away. We decided to turn it into a full open-source package you can run locally. Davia is designed for coding agents to generate an editable internal wiki for your project. It focuses on producing high-level internal documentation: the kind you often need to share with non-technical teammates or engineers onboarding onto a codebase. Writing this kind of documentation takes forever, diagrams are often missing, and most tools don’t let you edit everything locally or integrate smoothly with your workflow. Davia is fully open source. It produces an editable workspace: – Text lives in a Notion-like editor – Diagrams live on editable whiteboards – Everything runs locally and can be modified either in your IDE or in the workspace One thing people found especially interesting is that you can delegate the documentation to your IDE’s AI agent. Davia handles all the heavy lifting. Your agent just writes the docs, and Davia turns them into structured pages, diagrams, and visuals. The project is still early, and we’d love feedback, ideas, and examples of how you handle internal documentation today. GitHub: https://github.com/davialabs/davia Discord: https://discord.gg/A79mEzP8me

Visit product View on Hacker NewsNov 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, visual · Missing: mac, macos, cursor

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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