Airlist – A Native Outliner for iPhone and iPad

Hacker News

Hi everyone, just excited to share something I created and is now on the App Store. I love using outliners, but was frustrated by what existed, so I made my own. Airlist is 100% native, built with SwiftUI. It syncs across iOS, and the web (Mac app coming soon), has built in nested (AND/OR) filters, dates, tags, backlinks, dark mode, drag and drop, and a lot more. It’s free to download and try: https://apps.apple.com/app/id1572580423 Website: https://airlist.app

Visit product View on Hacker NewsMar 31, 2022

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1points

Did not reach leaderboard

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

89%89% 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: mac, apple, apps · Missing: agents, macos, agent

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: excited, exist, io · Missing: https docs, just released, lua

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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