Open-Source Notion UI, Lotion

Hacker News

My friend and I love the Notion UI, so we open-source a version we have been building. - Block-based editor - Drag to reorder blocks - Basic Markdown-parsing including bold, italic, headings and divider - Type '/' for command menu and shortcuts Tiny fun detail: When you move between blocks with your arrow keys, your cursor will remain at roughly the same horizontal position (vs jumping to the start or end of a block). Lotion is quite limited for now, and we would love any contributions (e.g. image blocks, video blocks, code blocks, etc.)

Visit product View on Hacker NewsJul 26, 2022

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Actual performance

59points

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Launch Intel predictions

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

85%85% 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: including · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · 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.

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

48%48% 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

22%22% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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