Finanzy – A personal budgeting app with location-based expense tracking

Hacker News

Hi HN! I'm a solo developer working on *Finanzy*, a personal finance app that helps people track income and expenses, create budgets, and generate visual reports — all in a clean and lightweight interface. One thing that sets Finanzy apart is its *location-based transaction tracking*. Users can optionally save where they made each expense and view everything on an interactive map — something that’s been really useful for frequent travelers and remote workers. Other features: - "Wants vs Needs" classification - Monthly summary with charts - Dark mode - Custom currency formatting (symbol placement, separators) - 10 languages supported (including Arabic, Turkish, Romanian) - One-time Lifetime Premium (no subscriptions) Just crossed 1000+ downloads on Android, and constantly improving based on feedback. Would love your thoughts or ideas. Thanks for reading!

Visit product View on Hacker NewsMay 24, 2025

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

82%82% 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.

TrustMRRFits verified-revenue profile · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, visual · Missing: mac, agents, macos

32%32% 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: ide, 000, io · Missing: https docs, excited, just released

21%21% 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 · Strong signals: subscription, active · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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