Wealthfolio 2.0- Open source investment tracker. Now Mobile and Docker

Hacker News

Hi HN, creator of Wealthfolio here. A year ago, I posted the first version. Since then, the app has matured significantly with two major updates: 1. Multi-platform Support: Now available on Mobile (iOS), Desktop (macOS, Windows, Linux), and as a Self-hosted Docker image. (Android coming soon). 2. Addons System: We added explicit support for extensions so you can hack around, vibe code your own integrations, and customize the app to fit your needs. The core philosophy remains the same: Always private, transparent, and open source.

Visit product View on Hacker NewsNov 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, dock · Missing: agents, agent, cursor

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, soon · Missing: plus, intuitive, reviews

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, 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.

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

22%22% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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