Slite (YC W18) Simple knowledge tool for teams now on mobile

Hacker News

Hi HN! We just released [Slite](slite.com) on iOS and Android 9 months after [our launch]( https://news.ycombinator.com/item?id=16476092 )! Slite is a super simple and carefully designed tool for teams to collaborate on their knowledge (wether it's meeting note or longer lasting pieces like wikis & handbooks). As one of our core promise is to enforce the habits of teams to write and share, mobile was an absolutely key piece. We really want Slite to be a universal tool used by all people in teams, which explains why we released on Android & iOS at the same time (the app is built in RN). Check it out and if you have any questions or feedback, we'd love to hear from you and all the team would be happy to help. Please share your thoughts in the comments! - [Playstore Link]( https://play.google.com/store/apps/details?id=com.slite.mobi... - [Appstore Link]( https://itunes.apple.com/app/slite/id1342934691?mt=8 ) - and if you want, feel free to upvote us on [ProductHunt]( https://www.producthunt.com/posts/slite-for-mobile ) Cheers! Chris

Visit product View on Hacker NewsDec 4, 2018

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

75%75% 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: ios · Missing: supports, reddit linkedin, podcasting

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

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

58%58% 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: ios, apps, month · Missing: mobile apps, personal, entrepreneurs

49%49% 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

48%48% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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