Snipset: local, AI-driven text expansion and clipboard manager
Expand text instantly with customizable keywords and on-device AI, using snippets, clipboard history, and a TOTP vault on your desktop. Iwan Kurniawan built Snipset for Windows power users, writers, and developers who want privacy-first automation. It runs a native Rust engine, keeps data in an encrypted SQLite database, and connects to Ollama for local model inference. This combination targets knowledge workers who need fast, offline text generation without cloud telemetry.
What Snipset actually does in practice
Snipset captures repetitive typing and automates insertion using customizable triggers like //email, stores snippets in an encrypted SQLite database, and preserves clipboard history for quick reuse. In addition, the app can transform or generate text via local Ollama models when those are present on the machine. The Vault integrates a TOTP authenticator so you can keep two-factor codes alongside other secure snippets in the same encrypted store.
How light it is on system resources
The native Rust engine keeps runtime footprint low, with a sub-15MB installer and a Tauri 2.x build that avoids webview overhead. The architecture uses an OS-level keyboard hook for the fastest trigger-to-text path, which explains the emphasis on minimal idle CPU and RAM usage. For users wanting minimal background load, the design places expansion and clipboard handling on the local process rather than an external service.
Is it safe and private to run on a desktop
Snipset stores everything locally and sends no telemetry, operating on a local-first principle and encrypted storage to keep prompts and clipboard items off third-party servers. AI processing is confined to on-device Ollama models, so prompts do not travel to external inference services. The app's one-click NSIS installer requires no external dependencies, reducing the attack surface during setup.
Practical pick for privacy-focused power users, with a setup trade-off
Snipset serves users who need rapid, offline text automation and want their snippets kept entirely on-device. The single practical drawback is that on-device AI features require a local Ollama installation and associated model management, which adds setup effort for those who only want basic expansion. For anyone prioritizing data sovereignty and fast triggers, the app performs as a focused automation tool.
