Ka

Kakeibo – a simple budget tracking app for simple people

Hacker News

Kakeibo – a simple budget tracking app for simple people

Hey HN! For the past few years I've been following the kakebo/kakeibo method to track my expenses and savings. The term might be a bit obscure, but it's really simple: plan your monthly spending based on your income and fixed expenses; track your expenses and categorise them in one of 4 categories (needs, wants, culture, unexpected); at the end of the month, reflect on your spending: look at aggregates by category, spot trends… overall, just be a bit more conscious about where you are spending your money, and use that to improve next month. Improving isn’t just about spending less money, but to actually spend it where it makes you the happiest. I was buying specific notebooks made for the method, and even if I love that “zen” moment of sitting down and filling it out daily or weekly, being a developer I needed to make it an app, obviously. I toyed with the idea 4 years ago while learning Svelte, but eventually discarded it like most of my hobby projects. However, last year I decided to get serious and build a real app, as simple as possible, so I could actually ship it. The goal was to translate the best parts of the method (tracking and reflection) to a digital format, and reap its benefits: always available, incredibly fast to track a transaction, and useful stats. On top of that, and extremely exhausted from the current trend of subscription apps, I wanted it to be as respectful as possible. One-time, small payment to get it, no upsells or ads, no weird permission requests, data always exportable and totally offline. On the technical side, it’s just an Expo app backed by a SQLite database, no other fancy technologies, no servers, no complex setup. I wanted it to be as lean as possible, and I’m pretty happy with the result and by how easy it is to maintain it. An added benefit of using an SQLite database is that one of the export options is to get the full database, and since you can also import data from one, you can basically hack it however you want in your computer, and get it back to the app. In the end, it’s your app, it’s your data, you should be free to do whatever you want with it. I spend a lot of time reading HN, so even if I’m quite scared of what might come out of this, I wanted to ask for some feedback from you. The app works perfectly fine for me (been using it daily since the year began) and for some of my friends, however, I’m having a bit of trouble getting it in front of people, mainly because I don’t really want to do paid advertisement or similar strategies. So if you are slightly interested, I’d really appreciate you trying out the app and sharing some thoughts. Since the app is paid, I have put up a page that gives you a promo code for the platform of your choice [1]. Thank you! [1] https://getkakeibo.com/hn

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
95%95% 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, computer, using · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, monthly · Missing: mobile apps, ios, personal
46%46% 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 · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
20%20% 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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