Notes, a very simple app which supports Textile Markup.

Hacker News

Please use "HN" as invite code. http://hmpgr.com/apps/notes I would love to know if there are folks who prefer Textile over Markdown. I know Markdown is more famous but are there Textile fans too? Notes app is built on Hmpgr Platform. Looking for feedback. I have been using it for sometime and find it useful (unbiased thought, I promise). Since the app itself is responsive, it works on all platforms (through browser). Please use "HN" as invite code in case you wish to try the app.

Visit product View on Hacker NewsMar 21, 2014

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2points

6comments

Did not reach leaderboard

Launch Intel predictions

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

78%78% 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: supports · Missing: reddit linkedin, podcasting, created

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

45%45% 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: apps · Missing: mobile apps, ios, personal

37%37% 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

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

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

Correct prediction on native model

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