Blotter – An interactive, never ending music video

Hacker News

One day I was listening to a playlist and wished there could be some cool visuals to go along with it. Blotter is a proof of concept I hacked together that does a bit of audio recognition combined with a few generative AI models (both text and img) to create visuals that are relevant to the song. The video stream is generated in real time at 24fps - you can try it yourself by requesting visuals in the Twitch chat using the "!v" command! Right now it's mostly a fun hack project, but I am tinkering with new model architectures for higher fidelity video as well as an interactive tool so people can make videos with their own audio files. I'd love to hear any feedback or suggestions, thanks!

Visit product View on Hacker NewsMay 22, 2023

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

91%91% 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, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

58%58% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: chat, real time, audio · Missing: web3, crypto, cryptocurrency

16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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