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Offline-first voice AI (<1 s latency, MLX, Apple Silicon)
Hacker NewsOffline-first voice AI (<1 s latency, MLX, Apple Silicon)
Hey HN, I built an offline-first voice AI that runs entirely on Apple Silicon using MLX + FastAPI. It achieves <1s end-to-end latency for speech-to-speech conversations, with a minimal UI. Repo: https://github.com/shubhdotai/offline-voice-ai Would love feedback on performance, model choices, and other ideas...
Share cardActual performance
2points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, using · Missing: mac, agents, macos93%93% 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: ide, io · Missing: https docs, excited, just released52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue28%28% 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, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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