This is how I scrape 99% websites via LLM

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AI Summary

Video Summary: Best Practices for Scripting Internet Data at Scale

Overview

• The video discusses best practices for scripting internet data at a large scale.
• It focuses on building a generic web scraper that can interact with browsers autonomously to complete web scraping tasks on platforms like Upwork.
• The presenter explains how AI, particularly large language models, has disrupted the web scraping industry.
• The video is divided into three parts, covering simple public websites, complex interaction-required websites, and websites requiring complex reasoning tasks.

Part 1: Simple Public Websites

• Simple public websites do not require authentication or payment to access.
• Large language models have made it easier to extract structured information from messy, unstructured data.
• Services like Fire CR, Gina AI, and Spidercloud optimize web content for large language models, converting HTML to markdown for easier processing.
• The presenter demonstrates an agent scraper that uses File C to convert website data to markdown and then uses a language model to extract specific information into JSON format.
• The presenter mentions a community with ready-to-use agent templates for web scraping and research.

Part 2: Complex Interaction-Required Websites

• Websites that require complex interactions, like logins or pop-ups, need human-like interaction to scrape data.
• Tools like Selenium, Puppeteer, and Playwright are used to simulate browser interactions.
• Agent QL is introduced as a tool to identify the right UI elements for interaction.
• The presenter walks through building a scraper for a job market website, including handling login forms and pagination.
• The scraper saves job posting data to Airtable or Google Sheets.
• This approach can be applied to other job posting websites with similar processes.

Part 3: Websites Requiring Complex Reasoning Tasks

• Some tasks require more sophisticated reasoning, like finding the cheapest flights or buying concert tickets within a budget.
• These tasks are more experimental and challenging to automate.
• Multi-O is mentioned as a platform exploring autonomous web agents for complex tasks.
• The presenter acknowledges that fully autonomous web agents still have a long way to go but are making impressive progress.

Conclusion

• The video concludes with an encouragement to try out the demonstrated techniques.
• The presenter invites viewers to join their community for detailed code breakdowns and templates.
• The community also offers support from other AI builders and updates on AI experiments.

Detailed Instructions and URLs

• No specific CLI commands or URLs were provided for direct action or access within the summary content.