Flowful – Ambient music generators for focus and concentration

Hacker News

Hey everyone, Flowful is a collection of ambient music generators designed to fade into the background while you work. I made Flowful out of frustration with ambient music playlists on streaming platforms either skewing my recommendations, or launching me into something very distracting once the playlist is finished. The music is procedurally generated real time in your browser using Javascript by piecing together a bunch of custom-recorded samples. Its free to try without an account, and the premium tracks rotate to become free on a weekly basis, meaning if you wait long enough, all the tracks would have had their turn on the free week. There are currently around 20 tracks (not including the combinations) and I plan on adding one or two new tracks each week (or whenever inspiration strikes). Most of the track art is made by DALL-E 2. Any and all feedback is very appreciated! :)

Visit product View on Hacker NewsJul 13, 2022

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

67%67% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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