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⏳ Timestamps
00:00 - Introduction to Crawl for AI and Cursor AI
00:37 - Overview of Crawl for AI: Features and Use Cases
01:07 - Key Features: Open Source, LLM-Friendly, Multi-URL Crawling
01:57 - Advanced Features: Custom JavaScript, Metadata, Proxy Support
02:44 - Installation Guide: Python and Playwright Setup
03:20 - Using Python for Crawl for AI: Quick Start Example
03:43 - Advanced Use Cases: JavaScript Execution, CSS Selectors, Proxies
04:17 - Comparison with Fire Crawl: Speed and Performance
05:18 - Introducing Stride AI-Powered Appointment Setters
05:58 - Setting Up Crawl for AI in Cursor AI
06:27 - Building an App: Scraping Programming Docs for LLMs
06:55 - Leveraging Crawl for AI for AI Coding Projects
07:50 - Integrating OpenAI with Crawl for AI: Step-by-Step
08:17 - Adding Explanations and Comments to Python Code
08:56 - Customizing Output: Markdown and JSON Data Extraction
09:50 - Running the Crawl: Example with Python and Mozilla Docs
10:19 - Troubleshooting Output and Saving Scraped Data
10:48 - Successful Crawl: Viewing Extracted Data from Python Docs
11:29 - Expanding Use Cases: Scaling Web Scraping with Crawl for AI
11:54 - Final Thoughts: Leveraging AI Tech for Web Scraping and Coding
12:25 - Community and Closing: Join Stride Community and Upcoming Content