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ChordHive – an Ultimate Guitar front end for bands

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ChordHive – an Ultimate Guitar front end for bands

I've always found Ultimate Guitar frustrating to use, especially when playing in a band. Simple things like making annotations, organizing songs into lists, or transposing chords are just a hassle, let alone trying to sync with your mates. That’s why I built ChordHive. It’s a straightforward tool where you can annotate chords and tabs, transpose them, autoscroll, and organize them with lists and tags. The key feature: everything updates in real-time, so everyone in your band stays on the same page. I’m a guitarist in a small band, so this comes from personal experience. But, I’d love to hear what features you’d find useful or how I could improve it. I’ll read every suggestion—your feedback really matters to me. (PS: I'm currently working on making the project ready for open-source: https://github.com/oskarkraemer/chordhive-web ) Thanks!

Visit productView on Hacker NewsDec 22, 2024
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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: songs · Missing: supports, reddit linkedin, podcasting
87%87% 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: open · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% 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: personal, way · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% 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
14%14% 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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