UPiano, a Piano in Your Terminal

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Hello HN! This is a Python app I've built for fun, while learning the Textual library, it's a piano/synthesizer app that you can play with the keyboard + mouse. You can see a demo video of it here: https://www.youtube.com/watch?v=0VXit110PcA You need to install FluidSynth before using it: https://www.fluidsynth.org/ It uses the Python bindings pyFluidSynth library: https://github.com/nwhitehead/pyfluidsynth And the General User GS soundfont: http://www.schristiancollins.com/generaluser.php If you try it out, i'd love to hear if you think it's cool, and if you have ideas on what could make it even cooler. =)

Visit product View on Hacker NewsAug 7, 2023

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos

28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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