ScreenMind: Privacy-First Local Screen Analysis with Gemma 4
ScreenMind is an open-source, local-only alternative to screen-aware AI tools. It uses Gemma 4 to analyze screen content and build a searchable memory without telemetry or cloud dependencies.
Why it matters
Privacy-conscious users want the benefits of screen-aware AI without the telemetry and data leakage risks of cloud-hosted solutions. ScreenMind offers a completely local, secure alternative.
TL;DR
- 01Operates entirely locally on consumer hardware, requiring a GPU with at least 4GB VRAM (6GB+ recommended for 3-5x speedup).
- 02Uses Gemma 4 as a single multimodal brain to analyze images, reason, and transcribe audio without Whisper.
- 03Secures data with a sensitive information filter and AES encryption for screenshots (via Fernet and the OS keyring).
Key facts
- Minimum VRAM
- 4GB
- Model Size
- ~5GB (Gemma 4 E2B GGUF)
- Encryption
- AES for screenshots
Local Architecture
ScreenMind operates entirely locally with no network calls post-download. It uses a tiered processing pipeline:
- Capture: Monitors for screen changes and handles deduplication.
- Analysis: Gemma 4 extracts app metadata, scene descriptions, and layout regions.
- Search: Hybrid search using
MiniLMembeddings andFTS5keyword indexing in a local SQLite database.
Hardware Scaling
The system was benchmarked on a 4GB VRAM GTX 1650, where model spilling to system RAM occurs. Moving to a GPU with 6GB+ VRAM enables the model to reside entirely in VRAM, providing a 3-5x performance boost.
Data Privacy
Sensitive information detection is handled via regex filters that mask credit cards, social security numbers, passwords, and API keys before they are stored in the SQLite database. Screenshots are secured using AES encryption (Fernet + OS keyring).
✓ When to use
- When you want a fully private, local alternative to cloud-dependent screen memory tools like Microsoft Recall.
- When you have at least 4GB VRAM (ideally 6GB+) and want a unified model for screen vision, audio memos, and reasoning.