Albums Vault, my take on how to manage Spotify album library

Hacker News

I am a long-time Spotify user, with tons of albums saved but never with a proper way to manage it: display genres, see what I've already listened, and many other things. I was finally able to bring to life the idea on how to handle the tons of music and it is named [Albums Vault]( https://codefloe.com/rabc/albums-vault/ ). It is a macOS app built with Tauri + Rust, with plans to release to more platforms soon. Since Spotify now restricts the creation of apps keys, it works in a BYOK: at first install the app instructs you on how to register for an app in Spotify and asks for your client ID.

Visit product View on Hacker NewsAug 31, 2026

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

85%85% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

55%55% 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, way · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

33%33% 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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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