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Trace – Offline Mac meeting transcription, overhauled from HN feedback

Hacker News

Trace – Offline Mac meeting transcription, overhauled from HN feedback

A couple of months ago I posted about Trace, a non-intrusive, shortcut-driven Mac app that records and transcribes your meetings on-device. I know, another meeting transcription app, but we've had a great response and I'm confident it fills a niche. Here's the original post: https://news.ycombinator.com/item?id=48521236 We've been working hard to bring community feedback on board, and the app is now so much more capable that it deserved a new post. Trace is still a minimal, out-of-the-way alternative to MacWhisper, but it's grown a lot of features since then, and the vast majority came from suggestions in the original HN thread. The biggest additions, to me, are below (full list here: https://traceapp.info/changelog ). - Trace used to live entirely in the menu bar and push everything out through the clipboard. It now has a History window to read, search, replay and manage every transcript in one place. From there you can correct transcription errors, label speakers and export to other apps. - Trace is now meeting-aware. When an app starts using the microphone, it prompts you to record, and you can teach it to auto-record on a per-app basis. It also offers to stop once the microphone is released, which was one of the most common requests (people kept forgetting to stop a recording when they'd hidden the pill). - There's a new in-person mode, so a meeting held in the same room is picked up through the mic and split correctly by speaker rather than coming out as one long monologue. - Speaker memory now persists between calls, so Trace can suggest names for voices it recognises. This massively speeds up naming participants. - Alongside marking key moments, you can now grab screenshots. Trace runs OCR on the capture and saves the text with the transcript, so it's searchable. - On-device summaries give you a short recap of the meeting, generated by a local model (Qwen by default, though you can switch it out or point Trace at your own OpenAI-compatible endpoint). - If you're about to join a call with people you've met before, Trace briefs you on what you discussed last time to get you up to speed. - Trace now detects the meeting language, so the live recap can translate in real time, and it handles non-English languages far better than before. - External audio and video files can now be imported and transcribed. - There's a separate tracecli tool for driving Trace from the command line. It includes "tracecli transcript --live --follow", which streams an accurate transcript while the meeting is still running. I use this to let a local LLM follow along live with a meeting. We also included a few quality-of-life fixes raised in the original thread. Audio is now written to disk as you record, so a mid-meeting crash can't corrupt the recording, the echo cancellation algorithm has been rewritten and there's a direct download for people who can't or don't want to use the App Store. Based on feedback that Trace was priced too low, we've also reworked the licensing. It's still a one-time lifetime licence with lifetime updates and no upgrade fees, but it now starts at £29 for a single Mac, with multi-Mac options too. There's also a 7-day free trial, so you can put it through its paces on a week of real calls before committing. Feedback is very welcome.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apps · Missing: agents, macos, agent
97%97% 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: para, compatible · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%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 · Strong signals: apps, video, month · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time, audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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