Ay

Ayapo – A Browser with Social Features (Built with Replyke)

Hacker News

Ayapo – A Browser with Social Features (Built with Replyke)

Hi HN, I’ve built a new browser that adds a social layer to the web. Every website can be upvoted, downvoted, or commented on, making it easier to gauge trustworthiness or discover niche sites, like back in the days. The home feed works like Reddit, letting users explore websites sorted by hot, top, new or controversial. The idea came from wanting to make the web more transparent and also exciting again. Technically, I built Ayapo using Replyke, which is a service I created to simplify building social apps for React & React Native. Replyke handled all the core features like feeds, comments, votes, in-app notifications, bookmarks and user management, letting me focus on what makes Ayapo unique and also on the UI. Would love to hear your thoughts and feedback. That said - Replyke is my main focus - Ayapo is simply a showcase of what Replyke can do. I plan to onboard some first developers to try out Replyke in a few weeks. If you think you can find use to the system I'd appreciate a sign up in the form I've setup over here so I can get in touch https://replyke.com To be clear, Replyke work perfectly fine and securely even if you already have user system in place, and just want to add the social features. App Store download: https://apps.apple.com/us/app/ayapo-social-browser/id6738983... Google Play download: https://play.google.com/store/apps/details?id=com.replyke.ay...

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
88%88% 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, apps · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, google, users · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, 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
45%45% 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.

Correct prediction on native model

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