Chat Apple Notes – RAG and Semantic Search for Apple Notes

Hacker News

Hey HN! We're Yash and Rohan and wanted to share a small weekend project built out of personal need. Chat Apple Notes brings semantic search and Retrieval Augmented Generation to Apple Notes on CLI using just your OpenAI API key for simplicity. The Apple Notes app, even after the Apple Intelligence updates, has no semantic search or RAG capabilities. Since there's no official API or export functionality for Notes, we used a workaround using AppleScript to extract notes, then created a simple CLI tool with three core features: - search: Semantic search across all your notes - ask: RAG-based Q&A using your notes as context - chat: Interactive chat that maintains context from previous conversations and references relevant notes The tool requires zero setup beyond an OpenAI API key (stored locally). No hosting needed - all vectors are stored in OpenAI's vector store. We focused on keeping it dead simple to setup and use. Demo: https://github.com/user-attachments/assets/3f62b195-d580-46c... GitHub: https://github.com/yashgoenka/chat-apple-notes Would love fellow HN'ers who use Apple Notes extensively to try it out!

Visit product View on Hacker NewsJan 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, user, context · 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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

56%56% 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: personal · Missing: mobile apps, ios, entrepreneurs

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

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

11%11% 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

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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