Mayo – A lightweight, web-based audio player that works offline

Hacker News

Here is the backstory: I used to use YouTube Music, but then I deleted my Google account. I didn't want Spotify because I hate it for various reasons, and at the same time I wanted to show off my programming skills. So here we are. --- On the client: solidjs + tailwindcss + minisearch. IndexedDB as the database. One web worker to download audio to OPFS. Another web worker for indexing with minisearch. On top of this a service worker to precache static assets and make the app work offline. On the server: bun + astro + sqlite + ffmpeg. Full-text search is implemented with fts5. Astro Actions is the best RPC. --- The submission link goes straight to demo. Here is the GitHub link: https://github.com/mayo-dayo/app Open to feature requests, but no promises.

Visit product View on Hacker NewsFeb 25, 2025

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

80%80% 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 · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · 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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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