Whatcha – social network for digital media

Hacker News

Hi all! Christian here, creator of Whatcha, the easiest way to discover, track, discuss, and share movies, shows, books, and podcasts. I created Whatcha because I wanted all the information I needed to decide what I should check out next in one place. If you're like me, you're having multiple conversations with friends on what they're watching, creating watch/read/listen lists on multiple platforms (or just a notes app) that quickly get disorganized, or just have a hard time deciding on what to start next. Whatcha is aiming to solve those problems by bringing everything and everyone together in one place. With the proliferation of media platforms, services, and content, Whatcha has been a great way to organize and share your favorites. I just launched Whatcha on the Apple app store, with an Android version coming soon. Would appreciate any feedback you have on how I can make Whatcha better and more useful for you. Thanks!

Visit product View on Hacker NewsJul 10, 2022

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24points

21comments

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

78%78% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

52%52% predicted probability of success on Product Hunt, 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

33%33% 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

16%16% 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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