Klaro Budget – Budgeting based on pay schedules

Hacker News

I got tired of budgeting apps that make you categorise every purchase. When I’m deciding whether to buy something, I don’t think “do I have money left in my dining budget?” I think “can I afford this before my next paycheck?” So I built Klaro Budget around pay schedules instead. You enter when you get paid and your recurring bills, and it shows you exactly how much disposable income you have for each pay period. No more guessing if that £50 dinner will mess up your monthly budget. It’s pretty simple but feels way more natural than the category-based approach everyone else uses. I’ve been using it myself and finally decided to clean it up for other people. Currently in beta and looking for the first 50 users to try it out. Happy to give everyone a free year when we officially launch in exchange for feedback. Link: https://www.klaro-budget.app

Visit product View on Hacker NewsJun 26, 2025

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

68%68% 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 HuntUnlikely to reach the leaderboard · Strong signals: apps, user, using · Missing: mac, agents, macos

43%43% 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, io · Missing: https docs, excited, just released

39%39% 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: apps, month, monthly · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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