PeerSplit–A free, P2P Splitwise alternative (local-first PWA)

Hacker News

Hey HN! I'm Tanay, and I just launched PeerSplit, a peer-to-peer, privacy-first app for splitting group expenses. I built it because I was pretty fed up with existing apps that track your data, throw ads in your face, or make you pay for "premium" features. So, I figured, why not build something local-first that respects your privacy? What’s different about PeerSplit: - No signup needed – It’s 100% free and works without collecting any personal data. - P2P syncing – Your data stays yours. Peer-to-peer collaboration means no servers storing anything. - Local-first – Works fully offline. - Cross-platform – Works on mobile, desktop, or whatever you’re using. - Super lightweight and designed for a no-nonsense UX that gets out of your way. - Oh, and yes, there’s dark mode. GitHub (fair-source licensed under FSL): https://github.com/tanayvk/peersplit

Visit product View on Hacker NewsOct 15, 2024

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

70%70% 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.

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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

12%12% 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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