Analyzing Web Indexes and User Behavior for Content Quality

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Utilizing web indexes based on user behavior could provide valuable insights on actual high-quality content that people value. For instance, analyzing where users spend their time online can help identify popular and valuable web pages. By focusing on the links that people share, it is possible to distinguish between high-quality content and content filled with ads or irrelevant information. Models trained to predict shared links prioritize authentic and valuable content over SEO-driven posts, as users tend to share content like recipes rather than SEO blog posts. Understanding user behavior and preferences through web indexes can significantly enhance content quality assessment.

In this episode, Nathan sits down with Will Bryk, CEO of Exa.ai. They discuss how Exa enables complex, research-based searches that differ from traditional keyword-based search, how their AI uses neural and non-neural methods, why they are using their own vector database, and more. Try the Brave search API for free for up to 2000 queries per month at https://brave.com/api

LINKS:

- Exa.ai: https://exa.ai/

X/SOCIAL:

@WilliamBryk (William)

@labenz (Nathan)

@ExaAILabs (Exa)

SPONSORS:

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TIMESTAMPS

(00:00:00) - Preview

(00:01:52) - Exploring the landscape of information retrieval tools

(00:14:19) - Exa: AI librarian for the web

(00:15:32) - Sponsors: Oracle | Omneky

(00:16:57) - AI Project Ideas

(00:22:26) - What does search built for AI mean?

(00:25:40) - Ranking with neural and non-neural methods

(00:30:50) - Sponsors: Brave | On Deck

(00:33:13) - Best practices for Exa prompting

(00:34:01) - The power of keyword search

(00:37:26) - Breaking down complex queries

(00:34:37) - The evolution of search engines

(00:39:53) - The business side of Exa

(00:40:17) - The challenges of building a web scale index

(00:39:53) - The business side of Exa

(00:50:59) - Vector DBs

(00:58:17) - Wrap

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