JotSense – AI note-taking agent that extracts notes from any source

Hacker News

I built JotSense, an AI note-taking agent that automatically captures insights from videos, meetings, documents, web pages, emails, databases, and more. I was frustrated with switching between tools just to organize my thoughts and save content. So I created JotSense to automate it all — for myself and now for others too. The key features are: - Cross-platform automated note-taking: Works with videos, meetings, docs, emails, databases, and more - AI assistant for chat, search, and organization - Integrations: Zoom, Google Meet, Gmail, Notion, Google Drive, etc. - Real-time transcription of meetings and calls - Reminders to revisit notes when it matters The goal is simple: Stop taking notes. Start taking action.

Visit product View on Hacker NewsApr 23, 2025

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

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: created · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews

47%47% predicted probability of success on AppSumo, 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

38%38% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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