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Discover and bookmark songs, books, movies, and food by mood

Hacker News

Discover and bookmark songs, books, movies, and food by mood

I wanted to go through every stage of launching a product, so I built a simple site that lets you discover and bookmark songs, books, movies, and food based on your mood. It has no fancy AI features, it's more like a lightweight, mood based Pinterest. I’m a developer and I built the whole thing using Cursor using an imperative approach instead of vibe coding declaratively. So even though the product itself is pretty minimal, the backend is well structured and it should give me the ability to implement future pivots fairly easily. It was mainly a learning exercise but I'd love to hear suggestions for directions to explore from here to make it actually valuable

Visit product View on Hacker NewsMay 26, 2025

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

69%69% 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: songs · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

46%46% 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 · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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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