A macOS menu-bar app that turns any conversation into a clean markdown transcript, with a local speech model running entirely on-device. One global shortcut brings up a small bar at the bottom of your screen. It captures your mic and the system audio as separate tracks, labels who said what, and lets you flag key moments mid-call that sit inline at the right timestamp. No bot joins the call, nothing leaves your Mac, no account, no subscription.
Hey Product Hunt 👋 I'm the maker. I built Trace, a Mac app that records and transcribes your meetings entirely on-device.
I built Trace for myself. I'd been using MacWhisper, but there was enough fiddling before each call that I'd forget to start it and walk out of an hour-long meeting with nothing written down. So the things I cared about most were that it's quick to activate and stays out of the way. You hit one global shortcut and a small bar appears at the bottom of your screen (there's also a keystroke to hide it entirely if you'd rather not see it while it's working). As it records your meeting you can flag anything important, with an optional note, as a "key moment". I built that because the "wait, that part matters" thought never survives to the end of a call. The key moments sit inline in the markdown at the right timestamp, so any AI you later paste the output into can see what mattered.
It records your mic and the system audio as two separate tracks, then runs the system side through on-device diarization so you get who said what rather than one blended wall of text. Your mic lines are labelled as you, and the other voices come out as Speaker 1, Speaker 2 and so on (speaker renaming is planned for a future update). The transcription, and the live recap while you're recording, both run entirely on your machine, and you can choose between two engines. One is fast, using NVIDIA's Parakeet-TDT (Core ML, on the Neural Engine). The other is more accurate, using WhisperKit and Whisper large-v3-turbo, and holds up better on accents, jargon and quiet rooms. Both download their weights once and then run locally.
There are no meeting bots to join the call. It just captures the audio your Mac is already playing out loud. Trace doesn't do any of the summarising itself, it just hands you clean markdown.
On privacy, the app is sandboxed and your audio never leaves the Mac. The only network call it has to make is on first run, downloading the speech and speaker models (around 500MB) from Hugging Face, and after that you can go fully offline and it keeps working. The one other thing that ever touches the network is an optional Google Calendar connection for auto-naming sessions, which is read-only and stays off until you turn it on. You don't need to create an account, and we don't collect any telemetry.
Trace is £9.99 once on the App Store, no subscription, and it needs macOS 14 or later on Apple Silicon since the models run on the Neural Engine.
If you've used Granola, Otter or Fathom, the difference is they put a bot in your call or do the work in the cloud, sometimes both, and I wanted neither.
I've been using it every day for months now and it's genuinely fixed the problem for me. Feedback very welcome, roasts included, and I'd love to hear whether it helps anyone else the way it helped me.
On-device transcription with no cloud round-trip is huge for sensitive calls — the use case I see most often is exactly the kind of conversation people don't want flowing through a SaaS pipeline. I host the ModeLoop Podcast on financial modeling and have wrestled with the privacy-vs-quality trade-off for years; the on-device speech models have finally crossed the threshold where local-first is a real choice, not a compromise. Following.
On-device speech is the hard part to get right, and the fact that nothing leaves the Mac is exactly why people end up trusting it. Splitting mic and system audio into separate tracks is a detail most tools skip. I've been through the same on-device vs cloud tradeoff doing local receipt scanning, accuracy is the thing that keeps you up at night.
How's the local model holding up next to the cloud ones?
Really nice idea 👍 How does the local speech model perform in noisy meetings or with multiple speakers overlapping?
Congrats on the launch, this looks super useful (and the fact that files never leave the computer + pricing is a one-time payment, not a subscription, is veryy refreshing). One question on the key moments feature: how easy is it to export or push those specific flagged spots into another note-taking tool like Notion after a call?
About Trace on Product Hunt
“No-frills offline meeting transcripts with context”
Trace launched on Product Hunt on May 26th, 2026 and earned 80 upvotes and 9 comments, placing #23 on the daily leaderboard. A macOS menu-bar app that turns any conversation into a clean markdown transcript, with a local speech model running entirely on-device. One global shortcut brings up a small bar at the bottom of your screen. It captures your mic and the system audio as separate tracks, labels who said what, and lets you flag key moments mid-call that sit inline at the right timestamp. No bot joins the call, nothing leaves your Mac, no account, no subscription.
Trace was featured in Notes (8.3k followers), Menu Bar Apps (12.2k followers) and Audio (2k followers) on Product Hunt. Together, these topics include over 9.4k products, making this a competitive space to launch in.
Who hunted Trace?
Trace was hunted by Alex Godbehere. 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.
Want to see how Trace stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Alex Godbehere