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Talk to your Mac offline – sub-second Voice AI (Apple Silicon and MLX)
Hacker NewsTalk to your Mac offline – sub-second Voice AI (Apple Silicon and MLX)
I wanted a voice assistant that feels realtime but runs completely offline. This prototype uses MLX + FastAPI on Apple Silicon to hit sub-second latency for speech-to-speech conversations. Repo: https://github.com/shubhdotai/offline-voice-ai It’s fast, minimal, and hackable — would love feedback on latency tricks, model swaps, or use-cases you’d like to see next.
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apple · Missing: agents, macos, agent94%94% 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: io · Missing: https docs, excited, just released51%51% 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, personal47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive41%41% 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, podcasting30%30% 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, revenue23%23% 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, crypto5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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