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Emotive Engine – Animation engine with musical time (not milliseconds)

Hacker News

Emotive Engine – Animation engine with musical time (not milliseconds)

Hey HN! I'm the creator of Emotive Engine. I've been working on this for 2+ years and finally open-sourced it today. The core idea: Most animation libraries work in milliseconds. Music works in beats. This creates a mismatch - hardcode a bounce for 500ms (perfect at 120 BPM), switch to 90 BPM, and everything drifts because 500ms is now 0.75 beats. Emotive Engine uses musical time as the atomic unit. Specify animations in beats, and they automatically become: - 500ms at 120 BPM - 667ms at 90 BPM - 353ms at 170 BPM Change tempo, everything adjusts. No recalculation needed. Built for AI interfaces (chatbots, voice assistants) but works for any real-time character animation. Pure Canvas 2D, 60 FPS on mobile, 2,532 tests passing. Live demo at https://emotiveengine.com/demo - the hero banner on GitHub was generated with the engine itself. Happy to answer any technical questions! MIT licensed.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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, io · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
33%33% 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, personal
29%29% 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, revenue
20%20% 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 · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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