splitty, the easiest way to split bills among friends

Hacker News

splitty's the easiest way to split receipts with friends. and today, i'd love to share the fastest, most accurate, and most powerful version yet with all of you at HN. splitting the bill at a restaurant is inefficient and unfair. it’s also annoying for your waiter. with splitty, take a photo of your receipt, select the food everyone ate, and it’ll charge your friends the exact amount they owe (incl. tax + tip) on Venmo. it also works with receipts in your camera roll (& soon, electronic receipts) and keeps a history of receipts you’ve split. in the last 2 weeks since launch, 200+ people have used splitty to split bills among friends. and their feedback has been incredible: "Just used splitty it’s so good" "The venmo thing is veryyyyy cool I dig that" "Tried it and it was so good!" "This is a great solution to an age-old problem!" "I can’t wait to continue using this!" 🪄 "Legitimately good experience Will seriously be recommending" check splitty out on the App Store. i'd love your feedback.

Visit product View on Hacker NewsMay 9, 2023

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

85%85% 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: using · Missing: mac, agents, macos

60%60% 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: io · Missing: https docs, excited, just released

49%49% 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: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

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

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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Import bills, split w/friends, charge on Venmo. AI powered.

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