The objective of this software is to automatically measure and analyze how time is spent on a computer by periodically capturing screenshots, using a vision-based language model (LLM) to describe the content, and summarizing the results to generate a high-level report of time allocation across applications and tasks.
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import os | |
from time import sleep | |
from openai import OpenAI | |
client = OpenAI( | |
# defaults to | |
api_key=os.environ.get("OPENAI_API_KEY"), | |
) | |
# Step 1: Create an Assistant |
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{ | |
"sections": [ | |
{ | |
"title": "Tyler Cowen's use of ChatGPT", | |
"summary": [ | |
"Uses ChatGPT on iPhone as a universal translator when traveling in foreign countries", | |
"Uses it to read menus, identify plants and animals, and get recommendations", | |
"On laptop, uses it to learn about obscure history and get background context for interviews", | |
"Asks follow-up questions to probe deeper and fact-check potential hallucinations" | |
], |
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{ | |
"sections": [ | |
{ | |
"title": "Introduction and Utility of ChatGPT", | |
"summary": [ | |
"The use of ChatGPT has significantly impacted the guest's personal and professional life, offering assistance in learning languages, understanding complex topics, and enhancing travel experiences.", | |
"ChatGPT can act as a universal translator, providing assistance with menu selection in foreign countries and helping identify unknown plants or animals.", | |
"The tool is seen as an integral part of life, deeply intertwined with the user's ability to work efficiently and learn new information." | |
], | |
"quotes": [ |
This document describes a command-line Python application for Automatic Time Tracking by Watching Computer Screen. The application periodically captures screenshots on macOS, processes them using a vision-based language model, and generates time-tracking reports.
The design covers:
- Overall architecture
- System components
- Data flows
- Implementation details
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