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Babeloop, a new music sight-reading webapp

Hacker News

Babeloop, a new music sight-reading webapp

Backstory: I have a kid learning to play the clarinet in a music conservatory, which involves compulsory sight reading classes. Teaching for sight reading is done on books. The idea for this app is to port the method online, so that it's easier to practice and follow one's progress. It should also be more fun, and, for those so inclined, competitive. The learning method is based on landmarks: for each clef one first learns the position of 2-3 landmarks, and then each note is in relation to a landmark. So for example, if you know where the middle G is on the treble clef, then you can learn fast that 2 positions up (next line) is B, and two positions down is E. (Anecdotally, in an earlier iteration of the app, landmark notes were displayed using specific colors; but users learned colors instead of the note position on the staff, and when they moved to a level without colors (or an actual score) they were completely lost.) The app doesn't try to teach keyboard playing, or fingering for any other instrument for that matter. It only helps associate the position of a note on the staff with a name, in a given clef. It doesn't deal with octaves: a C3 is a C4 is a C; or accidentals: a sharp G is a flat G is a G. It also doesn't wait for user input, as other apps do. Music doesn't work that way; but more importantly, the point is to learn to recognize intervals instantly, not count them. No account is necessary to use the app, only to participate in the leaderboard, and save one's score in case of device reset (or to use more than one device). When an account is created, the data is stored on the server in SQLite; I'm curious to see how far it can go. (Without an account, no data leaves the device.) It's still a little rough around the edges but should work ok in reasonably recent browsers. On the client side, it uses VexFlow to display notes, staff and clefs, but animations are done using CSS transitions (not JS), to be mobile friendly. Tone.js helps provide a more accurate timing than a simple setInterval. Icons are coded in SVG by hand; for simple shapes, this is surprisingly fun and straightforward to do.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
83%83% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, new · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, users, way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, users · Missing: plus, platform, intuitive
38%38% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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