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An infinite canvas notetaking app like OneNote with Markdown support

Hacker News

An infinite canvas notetaking app like OneNote with Markdown support

Hey HN, I'm Oskar. I used OneNote extensively for university and found the infinite canvas quite useful, but I ran into limitations, mainly around structure, technical workflows, and embedding capabilities. I ended up building something that fits my needs more closely: Notanic, an infinite-canvas note-taking application with a focus on technical use cases. Features: - real-time multiuser editing - native Markdown blocks (including code blocks) - precise sketching and graphing tools with measurements - unlimited hierarchical page nesting - embeds for Desmos, CodePen, GitHub gist, etc. - local-only usage or optional cloud sync It’s aimed at technical notes, architecture diagrams, math work, visual problem-solving, and mind-mapping. Available for free on desktop (Windows/macOS/Linux, unlike Onenote :( ) and also as a web app with cloud sync. Feedback is welcome. You can reach me at: krae0sk_765@outlook.de

Visit product View on Hacker NewsNov 28, 2025

Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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, including · Missing: https docs, excited, just released

75%75% 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 HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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