Open two-sided marketplace for fitness classes

Hacker News

Hi all, We've built a mobile app for what we hope to be a platform for fitness instructors, students, and studios to connect. We'd love to have your feedback. For students: We typically gather our wellness practices from a variety of sources. Rather than manage these sources independently, merge them with deepmove. Find your friends and your teachers and know where the best classes for your needs are, always. For teachers: Your organizing platform is here. Your community, your communications, your calendar. No more separate accounts for every facet of your teaching. Let deepmove simplify operations so you can focus on what you do best. For studios: Modernize your studio. Move from archaic systems behind the desk to a streamlined platform in a global marketplace. Cater to and interact with your clients and your teachers in a familiar format. Let your community shine. We’re just getting started!

Visit product View on Hacker NewsFeb 3, 2023

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

76%76% 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 HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

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

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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

15%15% 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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