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Astra – Automated savings and bank transfers

Hacker News

Astra – Automated savings and bank transfers

Hi Hacker News, Gil and Sam here, co-founders of Astra ( https://astra.finance ). Astra is mission control for your money and our new iOS app helps you achieve your personal savings goals — automatically. We built Astra because we were frustrated by the lack of smart tools out there to help manage our cash flow. Maintaining a complicated spreadsheet to manage our finances month to month and pay down debt as quickly as possible was a time-consuming headache. We built a web app last fall that mirrored a lot of the financial modeling we were doing manually but learned that our users didn’t just want to visualize their financial data; they also wanted to automate how they were spending, saving, and paying off debt. Enter our new iOS app. Our mission is to make your finances more intelligent, empowering, and personal. We’ve built this app so that you can easily and conveniently define transfers between your accounts, and automatically put funds aside for your savings and debt repayment goals. Whether you want to start a rainy day fund or earmark money for dozens of different goals, all you have to do is connect your bank accounts with your online credentials, create your savings goals, and manage the movement of your money with programmable “actions.” Just choose the accounts, the frequency, and the amount — and you can change any action whenever you want. From a technical perspective, we’ve tackled authentication, ACH transfer scheduling, and account virtualization (for as many savings goals as you’d like). Our planned next steps include automatic bill pay, more action inputs (down to the transaction level), shared goals between users, and dynamic transfer amounts with machine learning models we have ready to deploy (because our cash balances change week-to-week and month-to-month). We'd love to get feedback from the HN community on Astra so we can make our product even better. Thanks!

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6points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
88%88% 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: ios, personal, month · Missing: mobile apps, entrepreneurs, apps
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
54%54% 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: users · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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: finances, smart · 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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