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Wavvy – web-based audio editor (Audacity port)
Hacker NewsWavvy – web-based audio editor (Audacity port)
I originally developed a WASM port of wxWidgets for https://dj.app/ . When it came time to open source wxWidgets-wasm, I decided to port another complex app as a test case, and Audacity seemed like the obvious choice. In the process, I also needed to write a new host API for PortAudio for playback and recording in the browser. https://github.com/ahilss/wxWidgets-wasm https://github.com/ahilss/portaudio-wasm https://github.com/ahilss/wavvy
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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos74%74% 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: open source, ide, io · Missing: https docs, excited, just released59%59% 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, podcasting53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: widgets · Missing: mobile apps, ios, personal49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%15% 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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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