Lit.money – Ethically designed to be a private, simple way to see money

Hacker News

Hey HN People, I know it's 2025, and you might be wondering why there's a need for a personal finance web app. I'd like to share my story and why I created lit.Money. During COVID, I started actively managing my finances, primarily optimizing for FIRE and monitoring my progress. There are apps in India, but I didn't like their UI. I don't like to exchange messages or call with accountant for bank details or transaction queries. I also wanted a simple way to view my partner's and family's finances in one place. I considered Copilot, but it wasn't available in India. While there are 1 or 2 good apps available, none offer financial view sharing with partners, family, or accountants. I wanted a simple, user-friendly web app, so I built lit.money (currently available as a web app). Some key features: - View all your data on a single screen, including the number of transactions and accounts - Delete all your data with a single click (no need to contact support or wait) - "Finspace" -> a financial space where you can share your finances with partners, family, or accountants with customizable permissions (read/write) and account visibility - Easily bulk edit transactions and export data I'm not trying to create the next big thing, just a simple project that provides real value. I would greatly appreciate your feedback! There's a DEMO MODE available to try it easily, or if you're busy, you can check out some videos (thanks to my wife, she made them): https://www.youtube.com/@litmoneyapp

Visit product View on Hacker NewsApr 17, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, wife · Missing: supports, reddit linkedin, podcasting

94%94% 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: apps, user, single · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, video · Missing: mobile apps, ios, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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: active · Missing: arr, mrr, revenue

12%12% 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

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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