screenpipe

Rewind.ai captured desktop history through continuous video encoding that drained laptop batteries and locked personal data inside a proprietary macOS silo. Screenpipe replaces constant video recording with lightweight, event-triggered capture across Linux, macOS, and Windows. By feeding native accessibility trees and local Whisper transcripts into an on-device SQLite database, it gives developers searchable context without taxing workstation performance.

Why Screenpipe instead of Rewind.ai

Rewind.ai relied on continuous video streaming and aggressive OCR, imposing sustained CPU and GPU overhead during heavy engineering workloads. Screenpipe avoids that overhead with an event-driven engine written in Rust. Instead of capturing redundant video frames, it hooks into operating system events—window switches, mouse clicks, typing pauses, and scrolling—recording a frame only when display content changes. It extracts text directly from native OS accessibility trees, using OCR only when applications lack structured hierarchies. This architecture keeps CPU usage between 5 and 10 percent and RAM between 0.5 and 3 gigabytes, preserving machine capacity for compilers, test runners, and containers.

The developer footprint also differs. Rewind.ai offered a polished consumer timeline on macOS before shutting down its desktop product, with no direct access to indexed data. Screenpipe supports macOS, Linux, and Windows, exposing captured frames, audio transcripts, and SQLite FTS5 tables over a local REST API at localhost:3030. It includes an MCP server so assistants like Claude Code and Cursor can query active terminal output and browser research directly. The trade-off is operational maintenance: while Rewind ran invisibly, Screenpipe users must manage 5 to 10 gigabytes of monthly disk growth and configure their own local Whisper audio models.

What it does well

• Event-triggered display sampling pairs screenshots with accessibility tree metadata, minimizing CPU usage and battery drain compared to continuous video streams.
• Embedded REST API and MCP endpoints expose screen activity, window titles, and audio transcripts directly to editors like Cursor and Claude Code.
• On-device speech recognition transcribes microphone and system audio via Whisper models, producing timestamped records of standups and architectural reviews.
• Automations called Pipes run as scheduled markdown files, generating standup summaries, Linear ticket updates, or work recaps from desktop history.