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TabChop a truly painless way to split a tab

Hacker News

TabChop a truly painless way to split a tab

We built TabChop because splitting a bill still feels weirdly painful. Someone downloads an app, the receipt scan is off, the UI is clunky, and half the table gives up. After one too many dinners where the “solution” turned into unresolved requests and Venmo chaos, we wanted something that simply worked. TabChop starts with the receipt. Take a picture, share a short code, and everyone claims what they ordered. You can watch the receipt update live as people grab their items in real time, so there’s no “who got what?” confusion. We also store each person’s Venmo, Cash App, or Zelle info, so when the split is done it’s one click to pay. No awkward questions about who to pay or how much. The goal is simple. No friction, no math, no awkwardness. Just a painless way to split a tab.

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Actual performance

2points
4comments
Did not reach leaderboard

Launch Intel predictions

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TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
51%51% 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
48%48% 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
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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