Zenkan – Drag and drop your notes in flexible nested boards

Hacker News

Zenkan is a personal note-taking app with a 3-level nested board system. You can drag, resize, and arrange your notes and tasks in a flexible canvas with Markdown support (H1/2/3 titles, center text, code blocks, etc) for rich text formatting. I created this app because I found myself a bit overwhelmed with Notion's complex and rigid databases (especially their kanban view), while also thinking that Google Keep was not structured enough for my needs. Zenkan is a bit in the middle, I tried my best to add a nice drag-and-drop feel to it (also some satisfying sounds) while enforcing a nested structure for the cards. Zenkan can serve both as a Kanban for tasks and as a way to group related notes in a very visual way. Let me know if you have any questions or suggestions!

Visit product View on Hacker NewsApr 25, 2025

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

87%87% 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.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google, way · Missing: mobile apps, ios, entrepreneurs

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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