Go Budget, a full-featured manual budget tracker built with Flutter

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Over the weekend I launched Go Budget, a side project of mine from the last few years that originally was just an Android app used by me and my wife, but was recreated in Flutter during the pandemic. https://gobudget.io [0] App Store [1] Google Play [2] Privacy Policy Go Budget lets you set up your income and bills (repeating expenses), automates tracking them wherever possible, then makes it as painless as possible to log every day purchases. For individual users the app is free/adfree. I offer a premium subscription that allows you to back up budgets online and invite others to join your budgets to stay in sync. My experience with Flutter was nothing short of amazing, and I think the end result turned out pretty nice. I'm happy to answer any questions about Flutter or anything else related to the app. -- The backend stack is pretty basic: - Flask + Redis + Gunicorn + Traefik running in docker on a Digital Ocean VM - Managed Digital Ocean postgres cluster -- [0] https://apps.apple.com/us/app/id1531580468 [1] https://play.google.com/store/apps/details?id=io.gobudget [2] https://gobudget.io/privacy

Visit product View on Hacker NewsSep 28, 2020

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

92%92% 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: apps, google, users · Missing: mobile apps, ios, personal

59%59% 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

37%37% 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

36%36% 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: apple, google, apps · Missing: mac, agents, macos

33%33% 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: subscription · Missing: arr, mrr, revenue

21%21% 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

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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