Score In URL, a sheet music editor that store everything in URL

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I'm excited to publish my new project, [Score In URL]( https://powersnail.com/ScoreInUrl ), a simple, privacy-friendly, sheet music sharing service, where the entire content is encoded in the URL itself as a query parameter. It comes with a decent text editor, rendered score, and audio. The sheet music is notated with ABC Notation, rendered by abcjs. This project is heavily inspired by [Hashify]( https://hashify.me ), which encodes a markdown document into its URL. The code is here: https://github.com/PowerSnail/ScoreInUrl I made this with ViteJS and Tailwind CSS, and hosts it with GitHub Pages. I'm not proficient in front-end development at all, my past experience in this area being basically vanilla HTML/CSS, with a tiny bit of JS. This is in fact my first try at using any front-end framework. I sincerely welcome all feedback. The idea came to me when I tried to convey the idea of an exercise in a music forum. No forum I've used have native support for any form of sheet music. It's either typing "CDEFGAB", which isn't very readable and scales horribly for anything complex; or sharing an image, which is tedious because you have to open up an engraving software, export, and upload. And none of those approach has audio. You can read more about my thoughts [here]( https://powersnail.com/2022/score-in-url/ ).

Visit product View on Hacker NewsAug 5, 2022

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6points

Did not reach leaderboard

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

78%78% predicted probability of success on Indie Hackers, 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: excited, ide, io · Missing: https docs, just released, exist

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

13%13% 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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