macOS Screen Automation Assistant with LLM Integration
Budget: $30 – $250 USD
Project Overview
We are developing a macOS-based automation assistant designed to interact with a mobile application interface streamed to a desktop via Splashtop. The tool operates locally on a MacBook Pro and performs screen-driven automation using visual recognition and simulated user input.
This is intentionally designed as a half-automation system, where all key actions require explicit user confirmation.
Core Functionality
The application will:
• Run locally on macOS (terminal or lightweight GUI)
• Assume an active Splashtop session displaying an Android device
• Interact with the streamed interface using screenshot analysis and PyAutoGUI
Supported Actions
Across supported mobile applications, the system must handle:
• Profile Evaluation & Actions
Capture screenshots → send to LLM → receive structured evaluation → suggest action
• Swipe / Like Decisions
Apply rule-based scoring logic (1–10 scale)
Perform swipe actions based on configurable thresholds
• Message Assistance
Generate suggested messages using LLM
Support conversation initiation and continuation
Human-in-the-Loop Control
The automation must require confirmation:
• Before every swipe
• Before every message
User controls:
SPACEBAR → Confirm action
TAB → Regenerate suggestion
ESCAPE → Pause automation
RETURN → Resume automation
Configuration-Driven Behavior
System logic is controlled via text files:
• swipe-rules.txt → profile scoring logic
• text-examples.txt → conversation style guidance
• cv.txt → user persona/context
• config.txt → runtime options and feature toggles
Scope Constraints
• No app switching required
• Operates only on the currently visible application
• No handling of inbound likes / secondary views
• No deployment or remote hosting required
Technical Requirements
Python
PyAutoGUI
Screenshot / Image Recognition Techniques
LLM API Integration
State Machine / Input Handling Logic
macOS Environment
Primary Challenges
• Visual element detection reliability
• Stream latency and UI timing
• Robust automation stability
• Error recovery and fallback handling
Expected Outcome
A stable Version 1 assistant capable of semi-automated interaction with a visually streamed mobile interface, combining AI-assisted reasoning with explicit user oversight.
We are developing a macOS-based automation assistant designed to interact with a mobile application interface streamed to a desktop via Splashtop. The tool operates locally on a MacBook Pro and performs screen-driven automation using visual recognition and simulated user input.
This is intentionally designed as a half-automation system, where all key actions require explicit user confirmation.
Core Functionality
The application will:
• Run locally on macOS (terminal or lightweight GUI)
• Assume an active Splashtop session displaying an Android device
• Interact with the streamed interface using screenshot analysis and PyAutoGUI
Supported Actions
Across supported mobile applications, the system must handle:
• Profile Evaluation & Actions
Capture screenshots → send to LLM → receive structured evaluation → suggest action
• Swipe / Like Decisions
Apply rule-based scoring logic (1–10 scale)
Perform swipe actions based on configurable thresholds
• Message Assistance
Generate suggested messages using LLM
Support conversation initiation and continuation
Human-in-the-Loop Control
The automation must require confirmation:
• Before every swipe
• Before every message
User controls:
SPACEBAR → Confirm action
TAB → Regenerate suggestion
ESCAPE → Pause automation
RETURN → Resume automation
Configuration-Driven Behavior
System logic is controlled via text files:
• swipe-rules.txt → profile scoring logic
• text-examples.txt → conversation style guidance
• cv.txt → user persona/context
• config.txt → runtime options and feature toggles
Scope Constraints
• No app switching required
• Operates only on the currently visible application
• No handling of inbound likes / secondary views
• No deployment or remote hosting required
Technical Requirements
Python
PyAutoGUI
Screenshot / Image Recognition Techniques
LLM API Integration
State Machine / Input Handling Logic
macOS Environment
Primary Challenges
• Visual element detection reliability
• Stream latency and UI timing
• Robust automation stability
• Error recovery and fallback handling
Expected Outcome
A stable Version 1 assistant capable of semi-automated interaction with a visually streamed mobile interface, combining AI-assisted reasoning with explicit user oversight.