Lenma – A Voice Assistant for macOS

Hacker News

Hi HN, I’m Furkan. I built Lenma — a voice assistant for macOS that lets you control your Mac and your productivity tools using voice. Try it here: lenma.app Tech note: Wake word detection runs fully local using openWakeWord, integrated in Rust. What it does 1. Integrations (work apps) Google Calendar, Slack, Notion, Linear, Apple Notes, Mail, Calendar, Reminders - “What do I have today?” - “Create a Linear issue…” - “Send this to Slack…” 2. macOS control - “Set volume to 30%” - “Move this window to desktop 3” - “Split screen left and right” 3. Dictation anywhere. 4. Brain Book Save what you’re looking at on your screen into notes/memory - “Save this to Brain Book” 5. Real-time web search for grounded answers weather, currency, quick facts, etc. 6. Custom voice commands Trigger chains of bash/scripts, keyboard shortcuts, and clicks by voice. Why I built it I started Lenma after wrist/RSI issues forced me to reduce keyboard/mouse time. Dictation helped, but I wanted a voice interface that could take actions across macOS and my tools—not just type text. Feedback I’d love What’s the one macOS action you’d want to trigger by voice every day? For Raycast/Alfred power users: what would make voice control genuinely useful (and not annoying)? Thanks — I’ll be around to answer anything.

Visit product View on Hacker NewsJan 27, 2026

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor

99%99% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, google, answers · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 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.

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

25%25% 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, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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