Nodify – I built this because folders are where notes go to die

Hacker News

I was frustrated with how my notes became digital graveyards, so I built Nodify in my spare time. It automatically generates knowledge graphs from live meetings, research PDFs, and YouTube videos. Free, Open source (MIT) and uses Whisper API for transcription. Built with Node/Vue/Electron. Would love feedback from fellow makers. All data is stored locally and your OpenAI key is encrypted and never stored in the cloud. It is a locally running Electron app. Demo: https://nodify.app/ Repo: https://github.com/appliedUI/nodify

Visit product View on Hacker NewsJan 23, 2025

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3points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, notes, open · Missing: mac, agents, macos

75%75% 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 · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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