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ExtraBrain - local-first desktop copilot for live calls

Hacker News

ExtraBrain - local-first desktop copilot for live calls

Hi HN, I’m the maker of ExtraBrain. I built it because I kept running into the same problem in high-pressure live conversations: the call moves faster than my notes. By the time I’ve captured the prompt, constraints, edge cases, or action items, the conversation has already shifted. ExtraBrain is a Mac desktop app that gives you a private live workspace during interviews, meetings, lectures, and research calls. It can transcribe the session, keep track of context, help structure answers or follow-ups, and turn the session into notes afterward. A few things I cared about while building it: It’s local-first: transcripts, screenshots, prompts, and notes can stay on your Mac. It supports local transcription / local models where installed and compatible. You can bring your own OpenAI, Anthropic, Claude, Codex, or compatible provider access. It does not join your meeting as a bot. The free version is usable; Pro adds workflow/history/profile features. I know the interview-assistant category is sensitive. My intent is not to help people misrepresent their skills. I’m trying to build a tool for live thinking, accessibility, note-taking, preparation, and post-session review. People should still follow the rules of their interview, school, workplace, or platform. I’d especially love feedback on: whether the local-first/privacy model is clear enough whether the setup feels too heavy for a normal Mac user what responsible-use boundaries you’d expect from a tool like this whether “meeting copilot” or “interview copilot” better explains the product Mac version is available now: https://extrabrain.app Happy to answer questions, and genuinely curious where HN thinks this kind of tool should draw the line.

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, claude, model · Missing: agents, macos, agent
96%96% 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: supports, para, compatible · Missing: reddit linkedin, podcasting, created
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers, para · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
31%31% 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
13%13% 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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