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OpenKnowledge – open source AI-first alternative to Obsidian/Notion

Hacker News

OpenKnowledge – open source AI-first alternative to Obsidian/Notion

Hi HN, Nick here. We’re launching OpenKnowledge ( https://openknowledge.ai/ ), a “what you see is what you get” markdown editor that has direct integrations with Claude, Codex, and other agents. Available as MacOS app or Web UI+CLI. Fully free/local and OSS. We built this because we wanted a Notion-like experience for writing and sharing markdown files across our team. Obsidian is the best alternative we tried, but found it doesn’t have a true WYSWIG UI and it didn’t integrate well with Claude/Codex outside of community plugins. So we built OpenKnowledge. It takes shape as: 1. A MacOS app with a file navigator, the WYSIWYG editor, and link explorer. 2. Integrations with the Claude, Codex, and Cursor desktop apps. The agents can open an OpenKnowledge editor within their embedded web browsers for a side-by-side experience. 3. Built-in mcps, skills, and RAG for LLM-wiki and “AI Second Brain” scenarios + spec writing 4. An embedded terminal and CLI for TUI-first users OSS stack includes: Tiptap/prosemirror, CodeMirror, yjs (CRDT), Electron (MacOS app), Orama, remark/rehype/micromark/mdast, @pierre/trees On the architecture side, the interesting eng. challenges included: 1. A pipeline to convert ProseMirror to markdown in a bidirectional lossless way. ProseMirror uses ASTs, which are not designed to have byte-fidelity. 2. A dual-observer CRDT to keep the ProseMirror and markdown state in-sync. The CRDT + git also power a collaborative experience that shows what Agents are doing in the markdown, have undo/redo, and version history. The “Share” and cloud-sync functionality are geared for team collaboration. They feel “no-code” but leverage git/GitHub under the hood, which also means data stays fully private. In that spirit, we made OpenKnowledge open source for anybody who’s curious or who’d like to contribute. We’re actively thinking about plugins/extensibility and what’s next. If you have suggestions or feedback, would love to hear it.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: model, apple, agentic
100%100% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released
74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, users · Missing: mobile apps, personal, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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