Worldnotes – infinite notes in-browser canvas

Hacker News

I've spent the weekend hacking away on what I am calling "worldnotes". It's an infinite notes canvas, using wikilink style links to navigate around to different places. I was inspired by a demo of a digital art canvas that kept revealing gem after gem. I wanted to apply that concept to a website via a text editor. I've used obsidian for note-taking for a while now, and while appreciate it's publish feature, I wanted to take a swing at making a website with markdown based back-end. The worldnotes SDK has: * Markdown based notes, compatible with Obsidian or other markdown based editors that support wikilinks. * Lean editor core (just the basics to get you going) * Plugin system to extend editor capabilities * Full theming support via CSS tokens - easy to make your own * Ability to create custom storage adapters to serve content however you like You can checkout a demo here: (all changes are local) https://worldnotes.alexmason.me Disclaimer: AI was used in development - it's okay. The code won't hurt you.

Visit product View on Hacker NewsMay 24, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, notes, code · Missing: mac, agents, macos

91%91% 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: compatible · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

19%19% 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.

Incorrect prediction on native model

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