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Play Harmony Like a Stenographer

Hacker News

Play Harmony Like a Stenographer

I made a musical instrument called the Harmonic Keyboard. It lets you express harmony declaratively—you choose where you want to go harmonically, and it figures out the individual notes and how to move between them. The interface is inspired by stenography: you press up to three keys simultaneously, each controlling one axis—bass note, chord quality, and inversion. With just three fingers, you can access over 500 chords. Transitions between chords are automatically voice-led: notes move minimally and musically from one chord to the next. Under the hood, the system selects voicings by minimizing the Wasserstein distance between the previous and next chord—subject to the constraint that all required chord tones are present. I built this because I love harmony and music theory, and while I can play a bit of piano and guitar, my hands can’t keep up with my inner ear. This instrument meets me where I like to think—it handles the mechanics so I can focus on where I want the music to go. It works in the browser, no setup. Beginners can start right away by jamming on diatonic chords in C major. If you can touch type, you can play simple songs from tabs within minutes. It’s simple to start, but it opens up over time. If you’re an experienced musician, I especially hope you’ll try it—and give it time to click. There’s a bit of a learning curve, but if you can hear chords in your head and have an intuitive grasp of harmony, the instrument starts to feel like an extension of that instinct. For me, once it clicked, it subtly shifted how I think about chords—and even helped sharpen my ear. The three-axis layout gave me a new perspective on harmonic structure that’s carried over to other instruments, too. Here’s a video of me jamming with it: https://youtu.be/BKQ3owJ3COI Try it in the browser: https://harmonic-keyboard.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, notes, open · Missing: mac, agents, macos
84%84% 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
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, interface · Missing: plus, platform, reviews
66%66% predicted probability of success on AppSumo, 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
57%57% 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 · Strong signals: video, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
23%23% 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.

Incorrect prediction on native model

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