Summary 1. Project Overview We are building a local-first automation framework that captures user workflows on the desktop and translates them into autonomous browser actions. The project requires forking and integrating two major open-source repositories to create a secure, anonymized "observation-to-execution" pipeline. The Objective: Create a bridge where a local screen-capture engine feeds data into a PII-scrubbing layer, which then stores structured "Intent Nodes" in a cloud database to be executed by an AI browser agent. 2. Technical Stack Observation Layer: ScreenPipe (Rust / SQLite) Execution Layer: Stagehand (TypeScript / Playwright) Privacy Layer: Microsoft Presidio (Python / NLP) Database/State: Convex (Real-time syncing) Environment: Cross-platform (Windows/macOS), primarily Local-first. 3. Scope of Work and Deliverables Deliverable 1: The Secure ScreenPipe Fork Fork and Setup: Fork the ScreenPipe repository and implement a custom build pipeline. On-Device PII Scrubbing: Integrate Microsoft Presidio (or a performance-equivalent SLM) directly into the capture stream. Requirement: Text extracted via OCR or the Accessibility Tree must be scrubbed of PII (Names, IDs, SSNs, etc.) locally before it is stored or synced. Convex Sync: Wire the scrubbed metadata and accessibility events to a Convex database via streaming mutations. Deliverable 2: The Intent Node Generator Implement a logic layer that converts raw screen/accessibility events into a structured Intent Node JSON. Schema Requirements: Goal: The high-level objective detected. Intent: The specific next step intended. Context: The surrounding metadata (Active App, URL, Field Labels). Primary Action: The exact interaction (example: click Submit). Fallback: Alternative logic if the primary action fails. Deliverable 3: Stagehand LOCAL Integration Configure Stagehand to run in LOCAL mode (no Browserbase cloud dependency). Create a "Trigger" listener that watches the Convex database for new Intent Nodes and initiates a Stagehand "execute" or "act" command in a local Chromium instance. 4. Mandatory Requirements (Selection Criteria) Systems Proficiency: Proven experience with Rust (for ScreenPipe/Tauri internals) and TypeScript (for Stagehand/Playwright). Privacy Engineering: Experience with PII detection and NLP-based scrubbing (Presidio). Database Architect: Deep knowledge of real-time syncing (Convex) and vector-search implementation. Browser Automation: Expertise in Playwright/CDP (Chrome DevTools Protocol). Local-First Mindset: Understanding of resource management (CPU/RAM) for background desktop applications. 5. Definition of Done (Success Metrics) The project is considered complete when the following "Technical Loop" is functional: Capture: I perform a task on a web portal (example: a mock login). Scrub: The system captures the event, redacts my username/password locally, and sends the scrubbed metadata to Convex. Execute: Within less than 500ms, a separate Stagehand instance detects the new Intent Node in Convex and replicates the same action in a "Headless" local browser. Stability: The system maintains under 10 percent CPU usage on a standard 16GB RAM machine while active. 6. How to Apply Please provide: Links to your GitHub/Portfolio showing Rust or Playwright-based projects. A brief description of a time you integrated a local desktop app with a cloud-syncing database. Your estimated timeline for building the "Technical Bridge."