My first iOS app, WriteDown - a Markdown text editor

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Hello everyone, I taught myself Objective-C late last year, and relentlessly working my first, real, production app since then. My app has been approved for sale last week, it is now on version 1.1, but I still very excited day by day since the launch day. :) About WriteDown, it is a Markdown down editor specifically for the iPhone. You can write your text in Markdown or just plain text. Syncing: It support Dropbox syncing. Optionally, you can upload your pieces of writing to Google Drive or Evernote as well, so you can keep it in your favorite Cloud service(s). You can email them to yourself, or to your family member, or your colleagues. It is a .writedown binary file that includes all your current note(s) so that you can restore and or add it to current note list in the app. Ofcourse there are many many features, to learn more, visit its landing page [1] or the iTunes link. Important Note : WriteDown is NOT iOS 7 compatible at the moment, but I'm working on this. Works well on iOS 6.x Check it out here: https://itunes.apple.com/us/app/writedown-markdown-text-editor/id670733152?ls=1&mt=8 Let me know If you have any question. Thank you! ### [1] Landing page: http://vinhnx.github.io/writedown-site/ PROMO CODE I'm giving away a few copies to you, so here are 5 promo code you can redeem to install. Feel free to let me know if you have used one of them so that I can delete it :) 4TL7RNEHH9TM W3XX9HMEFML9 NTWHNY9YFJP4 YPRKPEKP6WFT LKX7HN7A396X

Visit product View on Hacker NewsJul 27, 2013

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6points

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, compatible · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, google, way · Missing: mobile apps, personal, entrepreneurs

51%51% 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

24%24% 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

23%23% 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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