We
We made an Audio ML sharing platform
Hacker NewsWe made an Audio ML sharing platform
Hey everyone we recently built a platform for people to share their audio ml creations and we are hoping to create it so people in the community can find demos of their models and eventually monetize their trained models. We are a team of small creators who are trying to improve the space for open source ML so the future is not dominated by closed source giants like ElevenLabs or chatgpt. if you have any feedback please feel free to share.
Share cardActual performance
2points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, chatgpt · Missing: mac, agents, macos92%92% 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 · Missing: supports, reddit linkedin, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released55%55% 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 · Strong signals: platform · Missing: plus, intuitive, reviews51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal42%42% 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, revenue17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, audio · Missing: web3, crypto, cryptocurrency3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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