LocalClicky is a Mac menubar app that lets you have a real conversation with your computer - completely offline. Say "Computer" to start a session. It stays listening. You chain commands back to back. Say "goodbye" when you're done. Everything runs on your machine: voice transcription, LLM multi models, VAD, macOS say No API keys. No subscription. No data leaving your Mac. MIT licensed.
Hey PH! I built LocalClicky because every voice assistant I tried made the same tradeoff: you get convenience, they get your data. Audio uploaded. Screenshots sent to a server. Commands logged.
LocalClicky flips that. The whole stack is local, Whisper for transcription, Ollama (qwen3 + gemma4) for reasoning and vision, macOS say for responses. Nothing phones home. Not your voice, not your screen, not your commands.
The session model is what makes it feel natural, you say "Computer" once and it stays with you (Just like Siri on steroids). Chain commands, ask follow up questions, say "bye" to end. VAD stops recording as soon as you stop talking, so there's no fixed timeout awkwardness.
It's open source and early. The top contributor priority right now is replacing the Google Speech Recognition wake word with something fully offline. GitHub in the comments, happy to answer anything.
Give it a try !!!
Love the local-first approach here. The session model also feels like the right UX for voice control, much closer to an actual conversation than one-shot commands.
Curious how you’re thinking about the safety layer before clicks or destructive actions, especially since everything can see/control the Mac locally. Is the plan to add confirmations for certain action types, or keep it lightweight and rely on retries/feedback?
the whisper + ollama stack being fully local makes the google wake-word the last phone-home piece — funny that the smallest model in the chain is the hardest one to cut the cord on. what's actually blocking the offline swap?
When LocalClicky decides it needs to “see the screen,” how do you keep that reliable and safe in practice—what guardrails exist to prevent mis-clicks or destructive actions, and how do you handle confirmation/retries in real workflows?
The fully local stack is the part that resonates with me. I work on voice AI for aging-in-place, and privacy comes up in nearly every conversation with families, because the data involved (health, daily routines, who stopped by) is about as sensitive as it gets. Keeping Whisper and the LLM on-device the way you have here removes a whole category of worry. One thing I am curious about: on older or lower-spec Macs, what is the realistic gap from end of speech to spoken response with the local models, and which model sizes stay usable? That latency is usually where local voice either feels like a real conversation or starts to break it.
About LocalClicky on Product Hunt
“Control your Mac with your voice locally”
LocalClicky launched on Product Hunt on June 5th, 2026 and earned 113 upvotes and 7 comments, placing #8 on the daily leaderboard. LocalClicky is a Mac menubar app that lets you have a real conversation with your computer - completely offline. Say "Computer" to start a session. It stays listening. You chain commands back to back. Say "goodbye" when you're done. Everything runs on your machine: voice transcription, LLM multi models, VAD, macOS say No API keys. No subscription. No data leaving your Mac. MIT licensed.
LocalClicky was featured in Open Source (68.5k followers), GitHub (41.2k followers), Tech (624.9k followers) and Audio (2.1k followers) on Product Hunt. Together, these topics include over 201.8k products, making this a competitive space to launch in.
Who hunted LocalClicky?
LocalClicky was hunted by Dikshant Rajput. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Dikshant Rajput