Track Budget – A simple, powerful personal finance tracke

Hacker News

I built Track Budget to simplify how I manage my finances — most tools felt bloated or unintuitive. This app aims to strike the balance between simplicity and power. Core features: Upload and parse bank or credit card statements (CSV, XLSX, XLS) AI-powered transaction categorization Shared expense tracking Budgets by category Clean, focused UI The app includes a 7-day free trial — no setup needed, just sign up and start exploring. It’s still under active development and I’d love feedback or feature ideas from early users. Feel free to email me directly at tomkolron@gmail.com. Thanks for taking a look!

Visit product View on Hacker NewsJun 21, 2025

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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

77%77% 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, users · Missing: mobile apps, ios, entrepreneurs

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: finances · 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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