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Medici, an open-source Splitwise-like app for managing expenses

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Medici, an open-source Splitwise-like app for managing expenses

Hey everyone! I've been working on this little app I'm calling Medici after getting annoyed with Splitwise only allowing me to submit a few expenses a day. It has all the main Splitwise features (debt simplification, different ways of splitting expenses, default splits, etc.) Would love feedback and just wanted to share! I'm self-hosting on my RaspberryPi and figured others might find it useful too :)

Visit productView on Hacker NewsJul 31, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
76%76% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
30%30% 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
14%14% 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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