SoundSeeker – Organize Your Musical Ideas

Hacker News

Hi HN, I've been programming for about three years, and this is my first full-stack web app. It's a tool for organizing musical ideas. Building and deploying it has been a great learning experience, and in that spirit I'd be grateful for any thoughts or suggestions if you care to take a look. Thank you! source: https://bit.ly/3giqrBU Why: Composing music is different for everyone, yet some practices are employed by many musicians. One such practice is to record a musical idea on a phone, capturing the idea at its freshest, and for many, clearest. This often leads to a lengthy catalogue of chronologically organized recordings that can be difficult to parse when sitting down to flesh out a piece of music – what matters most is the content of the idea, more so than when it was conceived. The purpose of SoundSeeker is to allow you to organize musical ideas based on their content instead of when they came to be, and to serve as an educational personal project in my growth as a software engineer. What: A graph-based organizational tool for scratch audio recordings Planned features: in-app audio recording, in-app audio trimming, custom labeling outside of the main tiered organizational system.

Visit product View on Hacker NewsOct 20, 2022

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6points

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Launch Intel predictions

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

79%79% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · 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

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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