Never forget, always find. Oblivion is a cutting-edge note-taking desktop application that transforms how you organize and retrieve your thoughts using the power of AI.
Perfect for researchers, developers, writers, and anyone who wants their notes to be truly searchable by meaning, not just keywords. Say goodbye to the frustration of knowing you wrote something but can't find it!
"Because your thoughts deserve better than basic text search."
- 🔍 AI-Powered Search: Understands meaning, not just keywords
- ⚡ Lightning Fast: Native Rust + Tauri backend
- 🔒 Local & Secure: SQLite database, no cloud required
- 🧠 Semantic Discovery: Search by concept and context
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Simply download the msi or exe installers from https://github.com/ThakurMayank5/Oblivion/releases/tag/v1.0.0
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Install and you are ready to go!
Clone the project
git clone https://github.com/ThakurMayank5/Oblivion.gitGo to the project directory
cd OblivionInstall dependencies
npm installStart developing
npm run tauri devOR
cd src-tauri
cargo tauri devTo build the application
- Download DLL files from here
- DLL Files
- Paste the DLL files inside the src-tauri directory
npm run tauri buildOR
cd src-tauri
cargo tauri buildSentences in Database
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“Rust is a systems programming language that guarantees memory safety.”
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“Tauri allows developers to build lightweight desktop apps with web technologies.”
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“The Eiffel Tower is one of the most famous landmarks in Paris.”
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“Cats often enjoy sitting in boxes because it makes them feel secure.”
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“Machine learning models can detect patterns in large amounts of data.”
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“Playing football is a popular weekend activity around the world.”
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“The human brain contains billions of neurons that communicate through synapses.”
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“I brewed a fresh cup of coffee to start my morning.”
Query: "artificial intelligence"
Result: → "Machine learning models can detect patterns in large amounts of data."
Frontend: React 19 + TypeScript + Tailwind CSS 4
Backend: Rust + Tauri 2.0
AI: rust-bert with sentence transformers
Database: SQLite with automatic migrations
Search: Cosine similarity on 384-dimensional embeddings
For support, email mayank.singh5t@gmail.com.
If you have any feedback, please reach out to me at mayank.singh5t@gmail.com
Contributions are always welcome!
Please open an issue or submit a PR.
