Use on-device AI to learn more from your book/article highlights

Hacker News

Hello HN, I decided to clean up and launch a side project I’ve been using to do more with my highlights from books and essays. I lean on semantic embeddings to do things like showing similar passages from other authors and the ability to do fuzzy/conceptual search queries. I find that viewing an idea from multiple angles can really help in digesting it. The demo mode also previews a rephrasing feature which is LLM-powered that I’ve found useful in grokking denser passages. I’m hoping to gauge interest in it before pursuing it further. Please let me know if you find this tool useful and how it could be improved!

Visit product View on Hacker NewsApr 29, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: grok, using · Missing: mac, agents, macos

79%79% 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.

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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 · 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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